Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation

Abstract

The Ye & scedil;il & imath;rmak River Basin in northern T & uuml;rkiye is crucial for the region's water supply, agriculture, hydroelectric power generation, and clean drinking water. The primary goal of this study is to determine which modeling approach is most appropriate for various locations within the basin and how well meteorological data can predict river flow rates. Hydrological and meteorological forecasting both depend on the prediction of river flow rates. An artificial neural network (ANN), Univariate and Multivariate Long Short-Term Memory (LSTM) models have been utilized for streamflow forecasting. This research aims to determine the best model for several provinces in the basin area and give decision-makers a tool for reliable river flow rate estimates by combining LSTM and ANN models. According to research findings, the supervised multivariate LSTM model performed better than the unsupervised model in accuracy and precision. The sliding window methodology is suitable for estimating river flow based on meteorological datasets because it offers a primary method for reinterpreting time-series data in a supervised learning style. Compared to LSTM models, the ANN model that has been statistically optimized through experiments (DoE) design performs better in forecasting the river flow rate in the Ye & scedil;il & imath;rmak River basin (R2 = 0.98, RMSE = 0.18). The study's findings provided prospective cognitive models for the strategic management of water resources by forecasting future data from flow monitoring stations.