UTILIZATION OF ARTIFICIAL NEURAL NETWORKS IN HYDROLOGICAL STUDIES: A COMPREHENSIVE REVIEW
Keywords:
Artificial Neural Network (ANN), Feed-Forward Neural Network, Hydrology, Rainfall-Runoff Modeling, Streamflow Forecasting, Water Quality, Groundwater.Abstract
This paper presents a thorough review of the use of Artificial Neural Networks (ANNs) in addressing hydrological challenges, offering a simpler and more efficient alternative to traditional computational methods, which are often complex and computationally intensive. ANNs, leveraging artificial intelligence, have been effectively applied in areas such as rainfall-runoff modeling, streamflow forecasting, water quality assessment, and groundwater management. A clear understanding of the hydrological processes being modeled is crucial for selecting appropriate input parameters and designing efficient ANN architectures. This review highlights various ANN applications, demonstrating their accuracy and utility in solving hydrological problems, making them a valuable tool for engineering applications.
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