Feng Lei, Khalil Umer, Aslam Bilal, Ghaffar Bushra, Tariq Aqil, Jamil Ahsan, Farhan Muhammad, Aslam Muhammad, Soufan Walid
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China.
ITC Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, the Netherlands.
Environ Res. 2024 Apr 1;246:118075. doi: 10.1016/j.envres.2023.118075. Epub 2023 Dec 28.
The current investigation examines the effectiveness of various approaches in predicting the soil texture class (clay, silt, and sand contents) of the Rawalpindi district, Punjab province, Pakistan. The employed techniques included artificial neural networks (ANNs), kriging, co-kriging, and inverse distance weighting (IDW). A total of 44 soil specimens from depths of 10-15 cm were gathered, and then the hydrometer method was adopted to measure their texture. The map of soil grain sets was formulated in the ArcGIS environment, utilizing distinct interpolation approaches. The MATLAB software was used to evaluate soil texture. The gradient fraction, latitude and longitude, elevation, and soil texture fragments of points were proposed to an ANN. Several statistical values, such as correlation coefficient (R), geometric mean error ratios (GMER), and root mean square error (RMSE), were utilized to evaluate the precision of the intended techniques. In assessing grain size and spatial dissemination of clay, silt, and sand, the effectiveness and precision of ANN were superior compared to kriging, co-kriging, and inverse distance weighting. Still, less than a 50% correlation was observed using the ANN. In this examination, the IDW had inferior precision compared to the other approaches. The results demonstrated that the practices produced acceptable results and can be used for future research. Soil texture is among the most central variables that can manipulate agriculture plans. The prepared maps exhibiting the soil texture groups are imperative for crop yield and pastoral scheduling.
本次调查研究了多种方法在预测巴基斯坦旁遮普省拉瓦尔品第地区土壤质地类别（黏土、粉砂和砂含量）方面的有效性。所采用的技术包括人工神经网络（ANN）、克里金法、协同克里金法和反距离加权法（IDW）。共采集了44个深度为10 - 15厘米的土壤样本，然后采用比重计法测量其质地。利用不同的插值方法在ArcGIS环境中绘制土壤颗粒集地图。使用MATLAB软件评估土壤质地。将点的梯度分数、纬度和经度、海拔以及土壤质地片段输入人工神经网络。采用了几个统计值，如相关系数（R）、几何平均误差率（GMER）和均方根误差（RMSE）来评估所采用技术的精度。在评估黏土、粉砂和砂的粒度及空间分布时，人工神经网络的有效性和精度优于克里金法、协同克里金法和反距离加权法。不过，人工神经网络的相关性仍低于50%。在本次试验中，反距离加权法的精度低于其他方法。结果表明，这些方法产生了可接受的结果，可用于未来的研究。土壤质地是影响农业规划的最核心变量之一。绘制的展示土壤质地类别的地图对于作物产量和畜牧规划至关重要。
