Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter...Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.展开更多
Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This stud...Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.展开更多
Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the respons...Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.展开更多
Accelerated soil erosion is anthropogenic phenomenon and a major worldwide environmental problem. It mainly leads to removal of the clay minerals and soil nutrients and thereby reduces soil fertility because of minera...Accelerated soil erosion is anthropogenic phenomenon and a major worldwide environmental problem. It mainly leads to removal of the clay minerals and soil nutrients and thereby reduces soil fertility because of mineralogical influence on the soil. The objectives of this study were to identify the dominant soil and rock minerals and the influences of mineralogical properties on soil erosion features. This study was conducted at the Merek watershed, located in Kermanshah, Iran. There are different geological formations comprising limestone, sandstone, radiolarite, flysch, shale and marl. The border of each formation was mapped based on geology map and was checked in the field, using GPS and digitized by GIS software (ILWIS 3.5). The erosion feature map was prepared through remotely sensed data (Landsat ETM+ 2002, Path/Row and acquired date). About 300 soil and 28 rock samples were collected from the study area for soil and mineralogy analysis. Result shows that inter-rill, rill and snow erosion were occurred mainly at soil from Sarvak, Ilam and Gurpi Formation which are mainly containing calcite, dolomite, quartz and caolinite minerals giving moderate soil erosion intensity (5 - 10 t·ha–1·yr–1). Whereas mica/smectite was dominant clay mineral of soil from Older Terraces resulting in gully erosion and considerable 12.90 t·ha–1·yr–1 soil loss. Furthermore, smectite was found as the dominant clay mineral from both soil and parent material of Kashkan Formation (marls material) contributing to landslide occurrence and severe annual soil erosion (16.6 t·ha–1·yr–1). This study revealed that both soil erosion feature and intensity potentially are affected by mineralogical properties.展开更多
In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (...In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (PGF) and adjacent vegetated soils (AVS) in mesic sites from three High Arctic islands in order to characterize microbial dynamics as affected by cryoturbation, and a broad bioclimatic gradient. We also characterize total biomass of soil microorganisms and the most probable number of bacteria along a topographic gradient within each bioclimatic subzone to evaluate whether differences in topography lead to differences in microbial dynamics at a smaller scale. We found total microbial biomass C, the most probable number of heterotrophic bacteria, and fungal genera vary along this bioclimatic gradient. Microbial biomass C decreased with increasing latitude. Overall, microbial biomass C, MPN and the number of fungal isolates were higher in AVS than in PGFs. The effects which topographic position had on microbial biomass C varied across the bioclimatic gradient as there was no effect of topographic position in Isachsen (subzone A) and Mould Bay (subzone B), when compared to Green Cabin (subzone C, warmer site).There was no effect of topographic position on MPN counts at Mould Bay and Green Cabin. However, in Isachsen, MPN counts were highest in the wet topographic position as compared to the mesic and dry. In conclusion, PGFs seem to decouple the effect climate that might have on the total biomass of soil microorganisms along the bioclimatic gradient;and influence gets ameliorated as latitude increases. Similarly, the effect of topography on the total microbial biomass is significant at the warmest bioclimatic zone of the gradient. Thus, climate and topographic effects on total microbial biomass increase with warmer climate.展开更多
Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effect...Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.展开更多
Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion.This study utilized rainfall simulation experiments to investigate the impact of four tillage methods(manual digging(MD),manua...Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion.This study utilized rainfall simulation experiments to investigate the impact of four tillage methods(manual digging(MD),manual hoeing(MH),traditional ploughing(TP),and ridged ploughing(RP))on runoff and soil erosion at the plot scale.The smooth slope(SS)was used as a benchmark.Rainfall intensities of 30,60,90,and 120 mm h−1 were considered.The study revealed that tillage altered rainwater distribution into depression storage,infiltration,and runoff.Tillage reduces runoff and increases infiltration.The four tillage methods(30–73%)increased the proportion of rainwater converted to infiltration to varying degrees compared to the SS(22–53%).Microrelief features influenced the role of tillage methods in soil erosion.Surface roughness and depression storage accounted for 79%of the variation in sediment yield.The four tillage methods reduced runoff by 2.1–64.7%and sediment yield by 2.5–77.2%.Moreover,increased rainfall intensity weakens the ability of tillage to control soil erosion.When rainfall intensity increased to 120 mm h−1,there was no significant difference in runoff yield among RP,TP,MH,and SS.Therefore,assessing the effectiveness of tillage in reducing soil erosion should consider changes in rainfall intensity.Additionally,the cover management(C)factor of the RUSLE was used to assess the effects of different tillage methods on soil loss.Overall,the C factor values for tilled slopes are in the order MH>TP>RP>MD with a range of 0.23–0.97.As the surface roughness increases,the C factor tends to decrease,and the two are exponential functions(R2=0.86).These studies contribute to our understanding of how different tillage methods impact runoff and soil erosion in sloped farmland and provide guidance for selecting appropriate local manual tillage methods.展开更多
The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeo...The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeochemical parameters,including discharge,specific conductance,pH,water tempera-展开更多
Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount ...Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.展开更多
The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through fie...The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.展开更多
基金financially supported by the National Basic Research Program of China (2009CB825105)the National Natural Science Foundation of China (41261090)
文摘Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.
基金Supported by the National Key Basic Research Development Pro-gram (2009CB421302 )National Natural Science Foundation ofChina (40861020,40961025,40901163)+1 种基金Natural Science Foun-dation of Xinjiang (200821128 )Open Foundation of State KeyLaboratory of Resources and Environment Information ystems(2010KF0003SA)
文摘Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.
文摘Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.
文摘Accelerated soil erosion is anthropogenic phenomenon and a major worldwide environmental problem. It mainly leads to removal of the clay minerals and soil nutrients and thereby reduces soil fertility because of mineralogical influence on the soil. The objectives of this study were to identify the dominant soil and rock minerals and the influences of mineralogical properties on soil erosion features. This study was conducted at the Merek watershed, located in Kermanshah, Iran. There are different geological formations comprising limestone, sandstone, radiolarite, flysch, shale and marl. The border of each formation was mapped based on geology map and was checked in the field, using GPS and digitized by GIS software (ILWIS 3.5). The erosion feature map was prepared through remotely sensed data (Landsat ETM+ 2002, Path/Row and acquired date). About 300 soil and 28 rock samples were collected from the study area for soil and mineralogy analysis. Result shows that inter-rill, rill and snow erosion were occurred mainly at soil from Sarvak, Ilam and Gurpi Formation which are mainly containing calcite, dolomite, quartz and caolinite minerals giving moderate soil erosion intensity (5 - 10 t·ha–1·yr–1). Whereas mica/smectite was dominant clay mineral of soil from Older Terraces resulting in gully erosion and considerable 12.90 t·ha–1·yr–1 soil loss. Furthermore, smectite was found as the dominant clay mineral from both soil and parent material of Kashkan Formation (marls material) contributing to landslide occurrence and severe annual soil erosion (16.6 t·ha–1·yr–1). This study revealed that both soil erosion feature and intensity potentially are affected by mineralogical properties.
文摘In this study, we determine differences in total biomass of soil microorganisms and community structure (using the most probable number of bacteria (MPN) and the number of fungal genera) in patterned ground features (PGF) and adjacent vegetated soils (AVS) in mesic sites from three High Arctic islands in order to characterize microbial dynamics as affected by cryoturbation, and a broad bioclimatic gradient. We also characterize total biomass of soil microorganisms and the most probable number of bacteria along a topographic gradient within each bioclimatic subzone to evaluate whether differences in topography lead to differences in microbial dynamics at a smaller scale. We found total microbial biomass C, the most probable number of heterotrophic bacteria, and fungal genera vary along this bioclimatic gradient. Microbial biomass C decreased with increasing latitude. Overall, microbial biomass C, MPN and the number of fungal isolates were higher in AVS than in PGFs. The effects which topographic position had on microbial biomass C varied across the bioclimatic gradient as there was no effect of topographic position in Isachsen (subzone A) and Mould Bay (subzone B), when compared to Green Cabin (subzone C, warmer site).There was no effect of topographic position on MPN counts at Mould Bay and Green Cabin. However, in Isachsen, MPN counts were highest in the wet topographic position as compared to the mesic and dry. In conclusion, PGFs seem to decouple the effect climate that might have on the total biomass of soil microorganisms along the bioclimatic gradient;and influence gets ameliorated as latitude increases. Similarly, the effect of topography on the total microbial biomass is significant at the warmest bioclimatic zone of the gradient. Thus, climate and topographic effects on total microbial biomass increase with warmer climate.
基金the National Natural Science Foundation of China(U1901601)the National Key Research and Development Program of China(2022YFB3903503)。
文摘Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.
基金jointly funded by the National Natural Science Foundation of China(41771259&41171034)Natural Science Basic Research Plan in Shaanxi Province of China(2023-JC-ZD-30&2019JZ-45)+1 种基金Shaanxi Provincial Water Conservancy Science and Technology Project(2016slkj-11)Shaanxi Provincial Key Laboratory Project of Department of Education(14JS059).
文摘Tillage methods play a crucial role in controlling rainwater partitioning and soil erosion.This study utilized rainfall simulation experiments to investigate the impact of four tillage methods(manual digging(MD),manual hoeing(MH),traditional ploughing(TP),and ridged ploughing(RP))on runoff and soil erosion at the plot scale.The smooth slope(SS)was used as a benchmark.Rainfall intensities of 30,60,90,and 120 mm h−1 were considered.The study revealed that tillage altered rainwater distribution into depression storage,infiltration,and runoff.Tillage reduces runoff and increases infiltration.The four tillage methods(30–73%)increased the proportion of rainwater converted to infiltration to varying degrees compared to the SS(22–53%).Microrelief features influenced the role of tillage methods in soil erosion.Surface roughness and depression storage accounted for 79%of the variation in sediment yield.The four tillage methods reduced runoff by 2.1–64.7%and sediment yield by 2.5–77.2%.Moreover,increased rainfall intensity weakens the ability of tillage to control soil erosion.When rainfall intensity increased to 120 mm h−1,there was no significant difference in runoff yield among RP,TP,MH,and SS.Therefore,assessing the effectiveness of tillage in reducing soil erosion should consider changes in rainfall intensity.Additionally,the cover management(C)factor of the RUSLE was used to assess the effects of different tillage methods on soil loss.Overall,the C factor values for tilled slopes are in the order MH>TP>RP>MD with a range of 0.23–0.97.As the surface roughness increases,the C factor tends to decrease,and the two are exponential functions(R2=0.86).These studies contribute to our understanding of how different tillage methods impact runoff and soil erosion in sloped farmland and provide guidance for selecting appropriate local manual tillage methods.
文摘The hydrogeochemical parameters of Jiangjia Spring,the outlet of Qingrnuguan underground river system(QURS) in Chongqing,were found responding rapidly to storm events in late April,2008.A total of 20 kinds of hydrogeochemical parameters,including discharge,specific conductance,pH,water tempera-
文摘Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.
文摘The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.
