A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall–Runoff Relationship in Water Resources Management

Abstract

Recently, Artificial Neural Network (ANN) methods, which have been successfully applied in many fields, have been considered for a large number of reliable streamflow estimation and modeling studies for the design and project planning of hydraulic structures. The present study aimed to model the rainfall-runoff relationship using different ANN methods. The Nergizlik Dam, located in the Seyhan sub-basin and one of the important basins in Turkey, was chosen as the study area. Analyses were carried out based on streamflow estimation with the help of observed precipitation and runoff data at certain time intervals. Feed Forward Backpropagation Neural Network (FFBPNN) and Generalized Regression Neural Network (GRNN) methods were adopted, and obtained results were compared with Multiple Linear Regression (MLR) method, which is accepted as the traditional method. Also, the models were performed using three different transfer functions to create optimum ANN modeling. As a result of the study, it was seen that ANN methods showed statistically good results in rainfall-runoff modeling, and the developed models can be successfully applied in the estimation of average monthly flows.

AI‐based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

“…The weights are modified after each iteration of the training set until the total error is below a predefined tolerance. A feedforward neural network with backpropagation is one of several neural network architectures that have been extensively employed in hydrological modeling (Tikhamarine et al, 2020; Turhan, 2021). Hence, a three‐layer neural network with backpropagation was used for streamflow modeling in this study.…”

Drenaj Alan-Oran Yönteminin Seyhan Havzası Akımlarına Uygulanması ve Sonuçların Kontur Grafikleri ile Değerlendirilmesi

“…Ortalama yıllık akış 211 m 3 /s dolaylarındadır (Özfidaner ve ark., 2018). Seyhan Havzası'nda yer yer Akdeniz iklimi ve karasal iklim özellikleri görülebilmektedir (Turhan, 2021). Seyhan Nehri ana su kaynağı olup, ana kollarını Göksu ve Zamantı Nehirleri oluşturmaktadır (Selek ve ark., 2016;Turhan, 2022).…”

Modeling runoff in Bhima River catchment, India: A comparison of artificial neural networks and empirical models

“…The findings demonstrated that the ANN technique offered a viable and viable substitute to conceptual models for simulations and forecasting in watershed modeling. Numerous investigations utilized the ANN for rainfall-runoff modeling, which is recognized as a black box technique (Roy & Singh 2020;Turhan 2021). The adaptive-network-based fuzzy inference system (ANFIS) model combines the inference process of fuzzy mathematics with the connectionist capability of ANN and has been applied for rainfall-runoff modeling for the past two decades .…”