Predicting total upland sediment yield using regression and machine learning models for improved land management and water conservation

Abstract

In this study, various regression models were utilized to predict total sediment yield in tons, while their performance was evaluated for accuracy and reliability. The dataset utilized contains numerous predictors that have been standardized and processed through principal component analysis to improve model performance. Models evaluated here include linear regression, normalized linear regression, PCA, PCC with generalized ridge regression, kernel ridge regression, multivariate regression, lasso regression approaches such as CA-ANN or ANN, and more. Results suggest that the artificial neural network (ANN) model achieved the lowest mean squared error (MSE), 113.641; this suggests superior predictive capability compared to other models. Although environmental data were complex and relationships complex, an ANN model showed less error, followed closely by CA-ANN with an MSE of 124.83. Traditional models such as linear or lasso regression revealed larger errors with negative squared values that indicated poor fits to data. This exhaustive analysis not only showcases the power of advanced machine-learning techniques in environmental modeling but also stresses the significance of selecting models based on data characteristics and specific environmental phenomena studied. Furthermore, its insights could assist environmental planners and advocates with better prediction and management of soil erosion and sediment transport for planning purposes and conservation efforts.