Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection...Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.展开更多
Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg...Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.展开更多
The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns o...The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.展开更多
Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data bas...Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.展开更多
The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta re...The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.展开更多
Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically ...Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.展开更多
BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their s...BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.展开更多
Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran...Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017.We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average(ARIMA)model to fit a univariate time series of animal bite incidence.The ARIMA modeling process involved selecting the time series,transforming the series,selecting the appropriate model,estimating parameters,and forecasting.Results:Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period.The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA(2,0,0)(1,1,1).This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years,suggesting that the incidence of animal attacks in the region would continue to increase during this time frame(2018-2019).Conclusion:Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences.The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year.Therefore,our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran.展开更多
In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spr...In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern China.Results show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,respectively.The regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu Spring.The auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological year.The delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological year.In addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are small.All these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.展开更多
Purpose: One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18 th place in the world based on the number of scholarly publications. The objective ...Purpose: One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18 th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council(TüB?TAK) to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.Design/methodology/approach: We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science(Wo S) database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series(ITS) analysis technique(also known as "quasi-experimental time series analysis" or "intervention analysis") to test if TüB?TAK's support program helped increase the number of publications. We defined ARIMA(1,1,0) model for ITS data and observed the impact of TüB?TAK's support program in 1994, 1997, and 2003(after one, four and 10 years of its start, respectively). The majority of publications(93%) were full papers(articles), which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.Findings: TüB?TAK's support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TüB?TAK's support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations.Research limitations: Interrupted time series analysis shows if the "intervention" has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the"intervention", other "event(s)" that might affect the time series data(e.g., increase in the number of research personnel over the years) should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher.Practical implications: TüB?TAK's "cash-for-publication" program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as "micropayments." Originality/value: Based on 25 years' worth of payments data, this is perhaps one of the first large-scale studies showing that "cash-for-publication" policies or "piece rates" paid to researchers tend to have little or no effect on the increase of researchers' productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs(based usually on bibliometric measures such as journal impact factors) and revising their reward policies.展开更多
Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used ...Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used to describe and predict the IDD related indicators,and spatial analysis was used to analyze the spatial distribution of salt iodine levels.Results In China,the median urinary iodine concentration increased in 1995–1997,then decreased to adequate levels,and are expected to remain appropriate in 2019–2022.The goiter rate continually decreased and is expected to be maintained at a low level.Since 2002,the coverage rates of iodized salt and the consumption rates of qualified iodized salt(the percentage of qualified iodized salt in all tested salt) increased and began to decline in 2012;they are expected to continue to decrease.Spatial epidemiological analysis indicated a positive spatial correlation in 2016–2018 and revealed feature regarding the spatial distribution of salt related indicators in coastal areas and areas near iodine-excess areas.Conclusions Iodine nutrition in China showed gradual improvements.However,a recent decline has been observed in some areas following changes in the iodized salt supply in China.In the future,more regulations regarding salt management should be issued to strengthen IDD control and prevention measures,and avoid the recurrence of IDD.展开更多
Objective:This paper aims to study the correlativity between the number of pulmonary tuberculosis (PTB) cases and seasonal meteorological factors in Beijing.Methods:Based on theory of Human-Environmental Inter Relatio...Objective:This paper aims to study the correlativity between the number of pulmonary tuberculosis (PTB) cases and seasonal meteorological factors in Beijing.Methods:Based on theory of Human-Environmental Inter Relation in Huangdi's Internal Classics,we adopted monthly cases of PTB in Beijing from 2004 to 2011,and established a Seasonal Autoregressive Integrated Moving Average (SARIMA) model.Using the cross-correlation function (CCF),we then analyzed the correlation between meteorological factors and number of infected patients.The related meteorological factors were subsequently integrated,to establish a Seasonal Autoregressive Integrated Moving Average with explanatory variables (SARIMAX) model,which was used to estimate and verify the number of PTB cases in 2012.Results:In this study,a SARIMA(0,1,1) (0,1,1)12 model was established;CCF analysis was used to reveal the correlativity between PTB and precipitation with 1 lag,relative humidity with 1 lag.Then,integrated with relative humidity with 1 lag (β =2.405,95% confidence interval:0.433-4.377),the SARIMAX prediction model was proved to be an accurate approach for predicting local situations of PTB occurrence.Conclusions:The occurrence of PTB is correlated with seasonal meteorological factors.Combining these factors,an exact prediction model can be established,to estimate of the number of PTB infected patients.展开更多
To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESD...To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.展开更多
FMS is a sort of highly automatic machining system,how to ensure part quality is master key to system highly active running.At first, series of machining dimension and process capability of flexible manufacturing syst...FMS is a sort of highly automatic machining system,how to ensure part quality is master key to system highly active running.At first, series of machining dimension and process capability of flexible manufacturing system(FMS), is analyzed.Result of its, strong self-correlation of data series shows that time series analysis is applicable to data series analyzed.Based on-line modeling and forecasting for data series, principle and method of feedback compensation control is proposed.On a foundation of the virtual instrument platform, Labview of national instrument (NI), FMS dimension and process capability monitoring system(monitoring system) is developed.In practice, it is proved that part quality and process capability of FMS are greatly improved.展开更多
The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the...The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.展开更多
Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze ...In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.展开更多
Unprecedented industrialization and urbanization have led to China’s poor energy efficiency. In response, the Chinese government has set goals to reduce energy consumption that may include implementing new tax polici...Unprecedented industrialization and urbanization have led to China’s poor energy efficiency. In response, the Chinese government has set goals to reduce energy consumption that may include implementing new tax policies. In this paper, we investigate the relationship between energy intensity, an indicator that measures the efficiency of energy consumption, and two sources of government revenue in China (i.e., value-added tax (VAT) and corporate income tax). As a case study, we developed a Granger co-integration model to analyze the dynamic relationship of energy intensity, VAT and corporate income tax in the non-ferrous metal industry, Jiangxi Province, China, between 1996 and 2010. Augmented Dickey-Fuller tests were used to validate the model. In our time series analyses, we found when controlling for corporate income tax, a one log unit increase of VAT resulted in a decrease of 1.17 log units of energy intensity. However, when controlling for VAT, a one log unit increase of corporate income tax resulted in an increase of 0.34 log units of energy intensity. Understanding the relationship between energy intensity and taxation in industries that consume high volumes of energy can greatly enhance China’s goal to reduce energy consumption. We believe our findings add to this on-going discussion.展开更多
For many years planning and management of water resources involved modeling and simulation of temporally sequenced and stochastic hydrologic events. Rainfall process is one of such hydrologic events which calls for ti...For many years planning and management of water resources involved modeling and simulation of temporally sequenced and stochastic hydrologic events. Rainfall process is one of such hydrologic events which calls for time series analysis to better understand interesting features contained in it. Many statistics-based methods are available to simulate and predict such a kind of time series. Autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are among those methods. In this study a search was conducted to identify and examine a capable stochastic model for annual rainfall series (over the period 1954-2015) of Debre Markos town, Ethiopia. For the historical series, normality and stationarity tests were conducted to check if the time series was from a normally distributed and stationary process. Shapiro-Wilk (SW), Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) tests were among the normality tests conducted whereas, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests were among the stationarity tests. Based on the test results, logarithmic transformation and first order differencing were performed to bring the original series to a normal and stationary series. Results of model fitting showed that three models namely, AR (2), MA (1) and ARMA (2,1) were capable in describing the annual rainfall series. A diagnostic check was performed on model residuals and ARMA (2,1) was found to be the best model among the candidates. Furthermore, three information criteria: Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC) were used to select the best model. In this regard, too, the least information discrepancy between the underlying process and the fitted model was obtained from ARMA (2,1) model. Hence, this model was considered as a better representative of the annual rainfall values and was used to predict five years ahead values. The mean absolute percentage error (MAPE) of the prediction was found to be less than 10%. Thus, ARMA (2,1) model could be used for forecasting and simulation of annual rainfall for planning, management and design of water resources systems in Debre Markos town.展开更多
Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial...Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial behavior of the Dead Sea through time. To achieve this aim, time series analysis has been performed to track this behavior. For this purpose, fifteen satellite imageries are collected from 1972 to 2013 in addition to 2011-ASTGTM-DEM. Then, the satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for the spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume. The study shows that the Dead Sea shrinks by 2.9 km2/year while the water level decreases by 0.65 m/year. Consequently, the volume changes by –0.42 km3/year. The study has also concluded that the direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to the nature of the bathymetric slopes. In contrast, no shrinkage is detected from the east direction due to the same reason since the bathymetric slope is so sharp. The use of the Dead Sea water for industrial purposes by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. The intensive human water consumption from the Jordan and Yarmouk Rivers for other usages is another main reason of this shrinkage in the area as well.展开更多
基金supported by the National Natural Science Foundation of China(Grants:42204006,42274053,42030105,and 41504031)the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy,Ministry of Education,China(Grants:20-01-03 and 21-01-04)。
文摘Singular spectrum analysis is widely used in geodetic time series analysis.However,when extracting time-varying periodic signals from a large number of Global Navigation Satellite System(GNSS)time series,the selection of appropriate embedding window size and principal components makes this method cumbersome and inefficient.To improve the efficiency and accuracy of singular spectrum analysis,this paper proposes an adaptive singular spectrum analysis method by combining spectrum analysis with a new trace matrix.The running time and correlation analysis indicate that the proposed method can adaptively set the embedding window size to extract the time-varying periodic signals from GNSS time series,and the extraction efficiency of a single time series is six times that of singular spectrum analysis.The method is also accurate and more suitable for time-varying periodic signal analysis of global GNSS sites.
文摘Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
文摘The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.
基金Supported by European Union-NextGenerationEU,Through the National Recovery and Resilience Plan of the Republic of Bulgaria,No.BG-RRP-2.004-0008-C01.
文摘Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways:Prediction and forecast.Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role.Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences.The time series analysis approach has the advantage of being easier to use(in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average).Still,it is limited in forecasting time,unlike the classical models such as Susceptible-Exposed-Infectious-Removed.Its applicability in forecasting comes from its better accuracy for short-term prediction.In its basic form,it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures(governments,companies,etc.).Instead,it estimates from the data directly.Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread;be it school closures,emerging variants,etc.It can be used in mortality or hospital risk estimation from new cases,seroprevalence studies,assessing properties of emerging variants,and estimating excess mortality and its relationship with a pandemic.
文摘The aim of this study is to establish the prevailing conditions of changing climatic trends and change point dates in four selected meteorological stations of Uyo, Benin, Port Harcourt, and Warri in the Niger Delta region of Nigeria. Using daily or 24-hourly annual maximum series (AMS) data with the Indian Meteorological Department (IMD) and the modified Chowdury Indian Meteorological Department (MCIMD) models were adopted to downscale the time series data. Mann-Kendall (MK) trend and Sen’s Slope Estimator (SSE) test showed a statistically significant trend for Uyo and Benin, while Port Harcourt and Warri showed mild trends. The Sen’s Slope magnitude and variation rate were 21.6, 10.8, 6.00 and 4.4 mm/decade, respectively. The trend change-point analysis showed the initial rainfall change-point dates as 2002, 2005, 1988, and 2000 for Uyo, Benin, Port Harcourt, and Warri, respectively. These prove positive changing climatic conditions for rainfall in the study area. Erosion and flood control facilities analysis and design in the Niger Delta will require the application of Non-stationary IDF modelling.
文摘Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.
基金Supported by the Key Scientific Research Project of Universities in Henan Province,No.21A330004Natural Science Foundation in Henan Province,No.222300420265.
文摘BACKGROUND Hepatitis B(HB)and hepatitis C(HC)place the largest burden in China,and a goal of eliminating them as a major public health threat by 2030 has been set.Making more informed and accurate forecasts of their spread is essential for developing effective strategies,heightening the requirement for early warning to deal with such a major public health threat.AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average(SARFIMA)for projections into 2030,and to compare the effectiveness with the seasonal autoregressive integrated moving average(SARIMA).METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023.Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality.Two periods(from January 2004 to June 2022 and from January 2004 to December 2015,respectively)were used as the training sets to develop both models,while the remaining periods served as the test sets to evaluate the forecasting accuracy.RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023.Overall,HB remained steady[average annual percentage change(AAPC)=0.44,95%confidence interval(95%CI):-0.94-1.84]while HC was increasing(AAPC=8.91,95%CI:6.98-10.88),and both had a peak in March and a trough in February.In the 12-step-ahead HB forecast,the mean absolute deviation(15211.94),root mean square error(18762.94),mean absolute percentage error(0.17),mean error rate(0.15),and root mean square percentage error(0.25)under the best SARFIMA(3,0,0)(0,0.449,2)12 were smaller than those under the best SARIMA(3,0,0)(0,1,2)12(16867.71,20775.12,0.19,0.17,and 0.27,respectively).Similar results were also observed for the 90-step-ahead HB,12-step-ahead HC,and 90-step-ahead HC forecasts.The predicted HB incidents totaled 9865400(95%CI:7508093-12222709)cases and HC totaled 1659485(95%CI:856681-2462290)cases during 2023-2030.CONCLUSION Under current interventions,China faces enormous challenges to eliminate HB and HC epidemics by 2030,and effective strategies must be reinforced.The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions,surpassing the capabilities of SARIMA.
文摘Objective:To explore the modeling of time series of animal bite occurrence in northwest Iran.Methods:In this study,we analyzed surveillance time series data for animal bite cases in the northwest Iran province of Iran from 2011 to 2017.We used decomposition methods to explore seasonality and long-term trends and applied the Autoregressive Integrated Moving Average(ARIMA)model to fit a univariate time series of animal bite incidence.The ARIMA modeling process involved selecting the time series,transforming the series,selecting the appropriate model,estimating parameters,and forecasting.Results:Our results using the Box Jenkins model showed a significant seasonal trend and an overall increase in animal bite incidents during the study period.The best-fitting model for the available data was a seasonal ARIMA model with drift in the form of ARIMA(2,0,0)(1,1,1).This model can be used to forecast the frequency of animal attacks in northwest Iran over the next two years,suggesting that the incidence of animal attacks in the region would continue to increase during this time frame(2018-2019).Conclusion:Our findings suggest that time series analysis is a useful method for investigating animal bite cases and predicting future occurrences.The existence of a seasonal trend in animal bites can also aid in planning healthcare services during different seasons of the year.Therefore,our study highlights the importance of implementing proactive measures to address the growing issue of animal bites in Iran.
基金This study is supported by the geological survey project:National Glacier and Desertification Remote Sensing Geological Survey(DD20190515)Youth Innovation Fund of China Aero Geophysical Prospecting and Remote Sensing Center for Natural Resources(2020YFL18).
文摘In order to study the hydrodynamic characteristics of the karst aquifers in northern China,time series analyses(correlation and spectral analysis in addition with hydrograph recession analysis)are applied on Baotu Spring and Heihu Spring in Jinan karst spring system,a typical karst spring system in northern China.Results show that the auto-correlation coefficient of spring water level reaches the value of 0.2 after 123 days and 117 days for Baotu Spring and Heihu Spring,respectively.The regulation time obtained from the simple spectral density function in the same period is 187 days and 175 days for Baotu Spring and Heihu Spring.The auto-correlation coefficient of spring water level reaches the value of 0.2 in 34-82 days,and regulation time ranges among 40-59 days for every single hydrological year.The delay time between precipitation and spring water level obtained from cross correlation function is around 56 days for the period of 2012-2019,and varies among 30-79 days for every single hydrological year.In addition,the spectral bands in cross amplitude functions and gain functions are small with 0.02,and the values in the coherence functions are small.All these behaviors illustrate that Jinan karst spring system has a strong memory effect,large storage capacity,noticeable regulation effect,and time series analysis is a useful tool for studying the hydrodynamic characteristics of karst spring system in northern China.
文摘Purpose: One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18 th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council(TüB?TAK) to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.Design/methodology/approach: We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science(Wo S) database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series(ITS) analysis technique(also known as "quasi-experimental time series analysis" or "intervention analysis") to test if TüB?TAK's support program helped increase the number of publications. We defined ARIMA(1,1,0) model for ITS data and observed the impact of TüB?TAK's support program in 1994, 1997, and 2003(after one, four and 10 years of its start, respectively). The majority of publications(93%) were full papers(articles), which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.Findings: TüB?TAK's support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TüB?TAK's support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations.Research limitations: Interrupted time series analysis shows if the "intervention" has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the"intervention", other "event(s)" that might affect the time series data(e.g., increase in the number of research personnel over the years) should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher.Practical implications: TüB?TAK's "cash-for-publication" program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as "micropayments." Originality/value: Based on 25 years' worth of payments data, this is perhaps one of the first large-scale studies showing that "cash-for-publication" policies or "piece rates" paid to researchers tend to have little or no effect on the increase of researchers' productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs(based usually on bibliometric measures such as journal impact factors) and revising their reward policies.
基金partly supported by the National Natural Science Foundation of China [81773370 and 82173638]the Natural Science Foundation of Heilongjiang Province [TD2019H001]
文摘Objective To recognize the spatial and temporal characteristics of iodine deficiency disorders(IDD),China national IDD surveillance data for the years of 1995–2018 were analyzed.Methods Time series analysis was used to describe and predict the IDD related indicators,and spatial analysis was used to analyze the spatial distribution of salt iodine levels.Results In China,the median urinary iodine concentration increased in 1995–1997,then decreased to adequate levels,and are expected to remain appropriate in 2019–2022.The goiter rate continually decreased and is expected to be maintained at a low level.Since 2002,the coverage rates of iodized salt and the consumption rates of qualified iodized salt(the percentage of qualified iodized salt in all tested salt) increased and began to decline in 2012;they are expected to continue to decrease.Spatial epidemiological analysis indicated a positive spatial correlation in 2016–2018 and revealed feature regarding the spatial distribution of salt related indicators in coastal areas and areas near iodine-excess areas.Conclusions Iodine nutrition in China showed gradual improvements.However,a recent decline has been observed in some areas following changes in the iodized salt supply in China.In the future,more regulations regarding salt management should be issued to strengthen IDD control and prevention measures,and avoid the recurrence of IDD.
基金This study was supported by the Natural Science Foundation of China(81574098).
文摘Objective:This paper aims to study the correlativity between the number of pulmonary tuberculosis (PTB) cases and seasonal meteorological factors in Beijing.Methods:Based on theory of Human-Environmental Inter Relation in Huangdi's Internal Classics,we adopted monthly cases of PTB in Beijing from 2004 to 2011,and established a Seasonal Autoregressive Integrated Moving Average (SARIMA) model.Using the cross-correlation function (CCF),we then analyzed the correlation between meteorological factors and number of infected patients.The related meteorological factors were subsequently integrated,to establish a Seasonal Autoregressive Integrated Moving Average with explanatory variables (SARIMAX) model,which was used to estimate and verify the number of PTB cases in 2012.Results:In this study,a SARIMA(0,1,1) (0,1,1)12 model was established;CCF analysis was used to reveal the correlativity between PTB and precipitation with 1 lag,relative humidity with 1 lag.Then,integrated with relative humidity with 1 lag (β =2.405,95% confidence interval:0.433-4.377),the SARIMAX prediction model was proved to be an accurate approach for predicting local situations of PTB occurrence.Conclusions:The occurrence of PTB is correlated with seasonal meteorological factors.Combining these factors,an exact prediction model can be established,to estimate of the number of PTB infected patients.
基金Project Supported by Cultiration Found of the Key Scientific and Technical Innovation Project,Ministry of Education of China(707018)
文摘To solve the problem of the flashover forecasting of contaminated or polluted insulator,a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper.The ESDD is the key of flashover on polluted insulator.The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper.The forecasting model consists of two artificial neural networks that reflect relationship of environment,ESDD and flashover probability.The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover.A series of artificial pollution tests show that the results of the forecasting model is acceptable.
基金This project is supported by Weaponry Advanced Fund Item of China (No.2000JS38.5.1 OT2001)
文摘FMS is a sort of highly automatic machining system,how to ensure part quality is master key to system highly active running.At first, series of machining dimension and process capability of flexible manufacturing system(FMS), is analyzed.Result of its, strong self-correlation of data series shows that time series analysis is applicable to data series analyzed.Based on-line modeling and forecasting for data series, principle and method of feedback compensation control is proposed.On a foundation of the virtual instrument platform, Labview of national instrument (NI), FMS dimension and process capability monitoring system(monitoring system) is developed.In practice, it is proved that part quality and process capability of FMS are greatly improved.
文摘The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
基金supported by the Technology Innovation Program(10083633,Development on Big Data Analysis Technology and Business Service for Connected Vehicles)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)。
文摘In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.
文摘Unprecedented industrialization and urbanization have led to China’s poor energy efficiency. In response, the Chinese government has set goals to reduce energy consumption that may include implementing new tax policies. In this paper, we investigate the relationship between energy intensity, an indicator that measures the efficiency of energy consumption, and two sources of government revenue in China (i.e., value-added tax (VAT) and corporate income tax). As a case study, we developed a Granger co-integration model to analyze the dynamic relationship of energy intensity, VAT and corporate income tax in the non-ferrous metal industry, Jiangxi Province, China, between 1996 and 2010. Augmented Dickey-Fuller tests were used to validate the model. In our time series analyses, we found when controlling for corporate income tax, a one log unit increase of VAT resulted in a decrease of 1.17 log units of energy intensity. However, when controlling for VAT, a one log unit increase of corporate income tax resulted in an increase of 0.34 log units of energy intensity. Understanding the relationship between energy intensity and taxation in industries that consume high volumes of energy can greatly enhance China’s goal to reduce energy consumption. We believe our findings add to this on-going discussion.
文摘For many years planning and management of water resources involved modeling and simulation of temporally sequenced and stochastic hydrologic events. Rainfall process is one of such hydrologic events which calls for time series analysis to better understand interesting features contained in it. Many statistics-based methods are available to simulate and predict such a kind of time series. Autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are among those methods. In this study a search was conducted to identify and examine a capable stochastic model for annual rainfall series (over the period 1954-2015) of Debre Markos town, Ethiopia. For the historical series, normality and stationarity tests were conducted to check if the time series was from a normally distributed and stationary process. Shapiro-Wilk (SW), Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) tests were among the normality tests conducted whereas, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests were among the stationarity tests. Based on the test results, logarithmic transformation and first order differencing were performed to bring the original series to a normal and stationary series. Results of model fitting showed that three models namely, AR (2), MA (1) and ARMA (2,1) were capable in describing the annual rainfall series. A diagnostic check was performed on model residuals and ARMA (2,1) was found to be the best model among the candidates. Furthermore, three information criteria: Akaike Information Criterion (AIC), the corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC) were used to select the best model. In this regard, too, the least information discrepancy between the underlying process and the fitted model was obtained from ARMA (2,1) model. Hence, this model was considered as a better representative of the annual rainfall values and was used to predict five years ahead values. The mean absolute percentage error (MAPE) of the prediction was found to be less than 10%. Thus, ARMA (2,1) model could be used for forecasting and simulation of annual rainfall for planning, management and design of water resources systems in Debre Markos town.
文摘Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial behavior of the Dead Sea through time. To achieve this aim, time series analysis has been performed to track this behavior. For this purpose, fifteen satellite imageries are collected from 1972 to 2013 in addition to 2011-ASTGTM-DEM. Then, the satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for the spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume. The study shows that the Dead Sea shrinks by 2.9 km2/year while the water level decreases by 0.65 m/year. Consequently, the volume changes by –0.42 km3/year. The study has also concluded that the direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to the nature of the bathymetric slopes. In contrast, no shrinkage is detected from the east direction due to the same reason since the bathymetric slope is so sharp. The use of the Dead Sea water for industrial purposes by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. The intensive human water consumption from the Jordan and Yarmouk Rivers for other usages is another main reason of this shrinkage in the area as well.
