Prediction of Soil Moisture Constants in Coastal Soils of Bangladesh Using Machine Learning Algorithms

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Abstract
The prediction of soil moisture constants (SMCs), including field capacity (FC), permanent wilting point (PWP) and available water content (AWC), is essential for efficient irrigation scheduling and water management in the coastal regions of Bangladesh affected by salinity, flooding and waterlogging. However, the comparative evaluation of machine learning (ML) models for predicting SMCs in these complex coastal soils remains limited. Therefore, this study evaluated five ML models to predict SMCs using soil physicochemical parameters (pH; electrical conductivity; organic matter; bulk density; and sand, silt and clay fractions). The observed ranges were 6.5%–38.4%, 1.3%–15.5% and 5.1%–26.0% for FC, PWP and AWC, respectively. Among the applied models, the multilayer perceptron (MLP) achieved the best predictive performance, with R 2 values of 0.926, 0.911 and 0.906 for FC, PWP and AWC, respectively, whereas MLR showed the weakest performance, with corresponding R 2 values of 0.804, 0.761 and 0.738. Compared with the other models, MLP improved R 2 by 3%–23% and reduced RMSE by 13%–40% across FC, PWP and AWC. Using input combination‐5 (IC‐5), the MLP explained more than 89% of the variability and achieved up to 98% prediction accuracy, highlighting its strong potential for predicting SMCs in coastal soils to support agricultural productivity.

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