Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major pr...Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System(GIS), Revised Universal Soil Loss Equation(RUSLE), Analytic Hierarchy Process(AHP), and machine learning algorithms(Random Forest and Reduced Error Pruning(REP) tree). RUSLE measured the rainfall eosivity(R), soil erodibility(K), length of slope and steepness(LS), land cover and management(C), and support practices(P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73%(715.94 km^(2)) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00-7.00×10^(3)kg/(hm^(2)·a), while about 7.46%(362.52 km^(2)) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00×10^(3)-48.00×10^(3)kg/(hm^(2)·a). Slope, elevation, stream density, Stream Power Index(SPI), rainfall, and land use and land cover(LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures.展开更多
Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the ...Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed methodologies.The Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for Knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field data.The Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil potential.According to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,respectively.The numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective techniques.This study will make an important contribution to evaluations of soil fertility and site suitability.This will help local government officials,academics,and farmers scientifically use the land.展开更多
The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D.Block using the Analytic Hierarchy Process(AHP)and Machine learning algorithms along with the field survey dat...The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D.Block using the Analytic Hierarchy Process(AHP)and Machine learning algorithms along with the field survey data and GIS.A total of 40 soil samples from Tulaipanji rice fields(from 0 to 40 cm depth)have been randomly collected for the analysis of the soil health condition.For the purpose of assigning ratings to the parameters,ten experts'opinions were taken into account.The final soil fertility map indicates that 18.01%of the land is in excellent health condition to support Tulaipanji cultivation.The artificial neural networks(ANN),support vector machine(SVM),and Bagging models-based suitability analysis was also done using geo-spatial and soil data for Tulaipanji cultivation.Nevertheless,the ANN is the more appropriate model for locational analysis of Tulaipanji cultivation.The ANN-based findings show that areas of 25.8%(77.89 sq.km)are excellent for growing Tulaipanji rice,about 22.01%(66.45 sq.km)are highly suitable,19.84%(59.90 sq.km)are moderately suitable,21.19%(63.97 sq.km)are low suitable and 11.16%(33.69 sq.km)are not suitable for Tulaipanji rice cultivation.The receiver operating characteristic(ROC)curve depicts that the applied models have a high degree of accuracy.This endeavour will aid much in the soil fertility and site suitability assessment that will aid local government officials,academics,and the framers,to utilize the lands in a scientific way.展开更多
文摘Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System(GIS), Revised Universal Soil Loss Equation(RUSLE), Analytic Hierarchy Process(AHP), and machine learning algorithms(Random Forest and Reduced Error Pruning(REP) tree). RUSLE measured the rainfall eosivity(R), soil erodibility(K), length of slope and steepness(LS), land cover and management(C), and support practices(P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73%(715.94 km^(2)) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00-7.00×10^(3)kg/(hm^(2)·a), while about 7.46%(362.52 km^(2)) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00×10^(3)-48.00×10^(3)kg/(hm^(2)·a). Slope, elevation, stream density, Stream Power Index(SPI), rainfall, and land use and land cover(LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures.
文摘Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land use.The primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed methodologies.The Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for Knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field data.The Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil potential.According to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,respectively.The numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective techniques.This study will make an important contribution to evaluations of soil fertility and site suitability.This will help local government officials,academics,and farmers scientifically use the land.
文摘The purpose of this work is to assess the soil fertility for Tulaipanji rice cultivation in Kaliyaganj C.D.Block using the Analytic Hierarchy Process(AHP)and Machine learning algorithms along with the field survey data and GIS.A total of 40 soil samples from Tulaipanji rice fields(from 0 to 40 cm depth)have been randomly collected for the analysis of the soil health condition.For the purpose of assigning ratings to the parameters,ten experts'opinions were taken into account.The final soil fertility map indicates that 18.01%of the land is in excellent health condition to support Tulaipanji cultivation.The artificial neural networks(ANN),support vector machine(SVM),and Bagging models-based suitability analysis was also done using geo-spatial and soil data for Tulaipanji cultivation.Nevertheless,the ANN is the more appropriate model for locational analysis of Tulaipanji cultivation.The ANN-based findings show that areas of 25.8%(77.89 sq.km)are excellent for growing Tulaipanji rice,about 22.01%(66.45 sq.km)are highly suitable,19.84%(59.90 sq.km)are moderately suitable,21.19%(63.97 sq.km)are low suitable and 11.16%(33.69 sq.km)are not suitable for Tulaipanji rice cultivation.The receiver operating characteristic(ROC)curve depicts that the applied models have a high degree of accuracy.This endeavour will aid much in the soil fertility and site suitability assessment that will aid local government officials,academics,and the framers,to utilize the lands in a scientific way.
