Department of Geo-Engineering, Andhra University, Visakhapatnam, 530003, India.
Department of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, 530003, India.
Environ Monit Assess. 2023 Jul 28;195(8):1006. doi: 10.1007/s10661-023-11613-y.
Soil salinization is a widespread phenomenon leading to land degradation, particularly in regions with brackish inland aquaculture ponds. However, because of the high geographical and temporal fluctuation, monitoring vast areas provides substantial challenges. This study uses remote sensing data and machine learning techniques to predict soil salinity. Four linear models, namely linear regression, least absolute shrinkage and selection operator (LASSO), ridge, and elastic net regression, and three boosting algorithms, namely XGB regressor, LightGBM, and CatBoost regressor, were used to predict soil salinity. Cross-validation was performed by splitting the data into 30% for model testing and 70% for model training. Multiple metrics such as determination coefficient (R), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) were used to compare the performances of these algorithms. By comparison, the CatBoost regressor model performed better than the other models in both testing (MAE = 0.42, MSE = 0.28, RMSE = 0.53, R = 0.92) and training (MAE = 0.49, MSE = 0.36, RMSE = 0.60, R = 0.90) phases. Hence, the CatBoost regressor model was recommended for monitoring soil salinity in India's massive inland aquaculture zone.
土壤盐渍化是一种广泛存在的现象，会导致土地退化，特别是在有咸水内陆水产养殖池塘的地区。然而，由于地理和时间上的高度波动，监测广阔的区域带来了巨大的挑战。本研究利用遥感数据和机器学习技术来预测土壤盐分。我们使用了四种线性模型，即线性回归、最小绝对值收缩和选择算子（LASSO）、岭回归和弹性网络回归，以及三种提升算法，即 XGB 回归、LightGBM 和 CatBoost 回归，来预测土壤盐分。通过将数据分为 30%用于模型测试和 70%用于模型训练，我们进行了交叉验证。我们使用了多个指标，如决定系数（R）、均方根误差（RMSE）、均方误差（MSE）和平均绝对误差（MAE），来比较这些算法的性能。相比之下，CatBoost 回归模型在测试（MAE = 0.42、MSE = 0.28、RMSE = 0.53、R = 0.92）和训练（MAE = 0.49、MSE = 0.36、RMSE = 0.60、R = 0.90）阶段的表现都优于其他模型。因此，我们推荐 CatBoost 回归模型用于监测印度内陆水产养殖区的土壤盐分。
