Improving satellite-based dissolved oxygen prediction for river management using a stepwise regression–neural network hybrid in Pahang River, Malaysia

Dissolved oxygen (DO) is a crucial indicator of river water quality, directly influencing aquatic ecosystem health and biogeochemical processes. Accurate estimation of DO in tropical river systems remains challenging due to limited in situ observations, high environmental variability, and persistent cloud cover that constrains satellite-based monitoring. This study addresses this gap by proposing a hybrid modelling approach that combines stepwise regression (SR) with two ANN architectures, Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN), to enhance DO prediction in the Pahang River, Malaysia, using Landsat 8 imagery. Stepwise regression was first applied to identify optimal predictor variables, reducing model complexity and omitigating overfitting before ANN training. The selected features were then used to develop and evaluate standalone ANN and hybrid SR–ANN models. Performance assessment using the coefficient of determination (R-squared) and root mean square error indicated that the hybrid SR–RBFNN achieved the best results, achieving an R-squared value of 0.991 and a root mean square error of 0.045 mg/L on the testing set. Analysis of hidden layer configurations further revealed that excessive neuron numbers may lead to overestimation, underscoring the importance of balanced network design. Spatial distribution maps of DO were generated for upstream, midstream, and downstream river segments, revealing distinct spatial variability influenced by salinity gradients and cloud contamination. Overall, the hybrid SR–ANN framework offers a cost-effective and robust solution for satellite-based DO estimation in data-scarce tropical regions. The approach demonstrates the value of combining feature selection with neural networks and supports digital decision-making for sustainable river basin management.

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