Optimization of Artificial Neural Network Transfer Function for Hydrological Modelling: A Review

Abstract

Hydrological modeling is crucial for understanding and predicting water-related processes. Artificial Neural Networks (ANN) have emerged as powerful tools for this purpose, utilizing the structure and functions of the biological brain to model complex patterns and forecast hydrological issues. Hence, this study reviews the optimization of artificial neural networks for hydrological modeling. Data obtained reveals that the choice of transfer function significantly impacts the performance of hydrological models, and optimizing it can improve accuracy, precision, and reliability. More so, an optimized transfer function provides interpretability, aligning with the physical understanding of the hydrological system and making the model outputs more meaningful. By optimizing artificial neural network transfer functions and employing other optimization strategies, hydrological models can better simulate and predict water-related processes. This advancement can lead to more effective water resource management and decision-making.