Work

Estimation of Rainfall-runoff Relationship Using Soft Computing Techniques

Year: 2025

Type: article

Abstract: The estimation of rainfall-runoff data is crucial for managing basins, preventing or mitigating flood disasters, making better use of water resources, and designing hydraulic structures.In this work, 1802 days of precipitation, runoff, and temperature data from the Quinapoxet River, which supplies the Wachusett Reservoir in the state of Massachusetts, USA, were used to develop models.The rainfall-runoff relationship was predicted using this data as input for Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Machines (SVM), and M5 Decision Tree (M5T) techniques.The results of each model were compared with the site measurement data separately.The models are compared with each other according to the three criteria, namely, the root-mean-squared error (RMSE), mean absolute error (MAE), and the correlation coefficient (R).It has been found that Artificial Neural Network (ANN) models perform better in runoff prediction compared to the other models.

Source: Applied Ecology and Environmental Research

DOI: 10.15666/aeer/2302_19231936

Author: H. GUZEL

Institution: İskenderun Technical University

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: diamond

Funder: U.S. Geological Survey