Brazil annually faces significant challenges with mass movements, particularly in areas with poorly constructed housing, inadequate engineering, and lacking sanitation infrastructure. Campos do Jordão, in Sã...Brazil annually faces significant challenges with mass movements, particularly in areas with poorly constructed housing, inadequate engineering, and lacking sanitation infrastructure. Campos do Jordão, in São Paulo state, is a city currently grappling with these issues. This paper details a study conducted within a pilot area in Campos do Jordão, where geophysical surveys and geotechnical borehole data were integrated. The geophysical surveys provided 2D profiles, and samples were collected to analyse soil moisture and plasticity. These datasets were combined using a Cokriging-based model to produce an accurate representation of the subsurface conditions. The enhanced modelling of subsurface variability facilitates a deeper understanding of soil behavior, which can be used to improve landslide risk assessments. This approach is innovative, particularly within the international context where similar studies often do not address the complexities associated with urban planning deficits such as those observed in some areas of Brazil. These conditions, including the lack of proper sanitation and irregular housing, significantly influence the geological stability of the region, adding layers of complexity to subsurface assessments. Adapting geotechnical evaluation methods to local challenges offers the potential to increase the efficacy and relevance of geological risk management in regions with similar socio-economic and urban characteristics.展开更多
[Objective] The aim was to explore evaluated precision on quality of soil environment polluted with zinc in agricultural production areas and to provide references for verification of production area.[Method] In Shula...[Objective] The aim was to explore evaluated precision on quality of soil environment polluted with zinc in agricultural production areas and to provide references for verification of production area.[Method] In Shulan City in Jilin Province,soils were sampled and analyzed in a laboratory using single-factor pollution index and GIS based spatial interpolation.The quality of environment polluted with zinc was assessed and related methods were compared according to Environment Quality Standard of Green Food Production Area.[Result] Spatial interpolation of zinc in soils based on GIS proved more precise than traditional methods;cokriging method with co-factors was higher in precision than common cokriging;cokriging method with zinc and organic matter was higher in precision than cokriging with zinc alone.[Conclusion] Quality assessment on environment polluted with zinc based on GIS interpolation is more scientific and reasonable than traditional methods.展开更多
Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of thi...Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.展开更多
文摘Brazil annually faces significant challenges with mass movements, particularly in areas with poorly constructed housing, inadequate engineering, and lacking sanitation infrastructure. Campos do Jordão, in São Paulo state, is a city currently grappling with these issues. This paper details a study conducted within a pilot area in Campos do Jordão, where geophysical surveys and geotechnical borehole data were integrated. The geophysical surveys provided 2D profiles, and samples were collected to analyse soil moisture and plasticity. These datasets were combined using a Cokriging-based model to produce an accurate representation of the subsurface conditions. The enhanced modelling of subsurface variability facilitates a deeper understanding of soil behavior, which can be used to improve landslide risk assessments. This approach is innovative, particularly within the international context where similar studies often do not address the complexities associated with urban planning deficits such as those observed in some areas of Brazil. These conditions, including the lack of proper sanitation and irregular housing, significantly influence the geological stability of the region, adding layers of complexity to subsurface assessments. Adapting geotechnical evaluation methods to local challenges offers the potential to increase the efficacy and relevance of geological risk management in regions with similar socio-economic and urban characteristics.
基金Supported by National 973 Program(2010CB951500)National 863 Program(2006AA-120103)~~
文摘[Objective] The aim was to explore evaluated precision on quality of soil environment polluted with zinc in agricultural production areas and to provide references for verification of production area.[Method] In Shulan City in Jilin Province,soils were sampled and analyzed in a laboratory using single-factor pollution index and GIS based spatial interpolation.The quality of environment polluted with zinc was assessed and related methods were compared according to Environment Quality Standard of Green Food Production Area.[Result] Spatial interpolation of zinc in soils based on GIS proved more precise than traditional methods;cokriging method with co-factors was higher in precision than common cokriging;cokriging method with zinc and organic matter was higher in precision than cokriging with zinc alone.[Conclusion] Quality assessment on environment polluted with zinc based on GIS interpolation is more scientific and reasonable than traditional methods.
基金Foundation item:Under the auspices of Shahrood University of Technology,Iran(No.348517)
文摘Soil macronutrients(i.e. nitrogen(N), phosphorus(P), and potassium(K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks(ANN) and two geostatistical methods(geographically weighted regression(GWR) and cokriging(CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil(0–30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration(n = 84) and validation(n = 22). Chemical and physical variables including clay, p H and organic carbon(OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model(with coefficient of determination R^2 = 0.922 and root mean square error RMSE = 0.0079%) was more accurate compared to the CK model(with R^2 = 0.612 and RMSE = 0.0094%), and the GWR model(with R^2 = 0.872 and RMSE = 0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients(N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.
