Work

Streamflow prediction using machine learning approaches with different shared socioeconomic pathways (SSPs)

Year: 2025

Type: article

Abstract: Streamflow prediction is crucial for effective water resource management and flood prediction. Therefore, this study aims to predict streamflow within the Klang river catchment. Two machine learning approaches, namely artificial neural network (ANN) and support vector machine (SVM), were employed to forecast streamflow within the Klang river catchment. The performance of each model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and percentage error. SVM outperformed ANN in streamflow prediction, achieving the lowest values of MAE, RMSE and percentage error, recorded as 7.23, 9.03 and 19.24, respectively. The model was then used to run the future scenarios, under two shared socioeconomic pathways (SSPs), which are SSP2-4.5 and SSP5-8.5, from coupled model intercomparison project phase 6 (CMIP6). SSP5-8.5 displays greater fluctuations than SSP2-4.5. This heightened variability evident in SSP5-8.5 can be attributed to its premise of rapid population expansion, significant technological advancements, and inadequate measures to address environmental issues. Consequently, these factors contribute to more frequent occurrences of extreme climate events.

Source: International Journal of Applied Science and Engineering

DOI: 10.6703/ijase.202506_22(2).003

Authors: Ren Jie Chin, Ling Yong, Yuk Feng Huang, Lloyd Ling, Chai Hoon Koo, Ooi Kuan Tan

Institutions: Universiti Tunku Abdul Rahman Sungai Long Campus, Universiti Tunku Abdul Rahman

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $200