Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport

Abstract

The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the Godavari River Basin. The dataset was divided into pre-1990 (1969–1990) and post-1990 (1990–2020) periods, revealing a significant decline in mean annual SSL from 136.85 to 62.38 million tons post-1990 due to anthropogenic influences such as dam construction and land-use/land-cover (LULC) changes. Despite a consistent seasonal distribution (~ 73% SSL contribution from monsoon months in both periods), there was a notable decline in median and peak SSL values, along with a narrowing interquartile range, indicating reduced sediment availability. The empirical cumulative distribution function (ECDF) further revealed shifts in sediment transport, with post-1990 SSL values surpassing pre-1990 levels at higher cumulative probabilities, suggesting altered sediment retention and release patterns. To improve SSL prediction, tree-based ML models were developed and evaluated using R², RMSE, and MAE metrics. Among them, the extra trees regressor (ETR) demonstrated the highest predictive accuracy (R² = 0.97 in training, 0.9 in testing) with the lowest errors, while the random forest regressor (RFR) and gradient boosting regressor (GBR) provided competitive results. The findings highlight the impact of human modifications on sediment transport and emphasize that ensemble tree-based models offer a robust solution for SSL prediction. This study provides valuable insights for river basin management and sustainable sediment transport modeling under changing hydrological conditions.