A data-driven approach to river discharge forecasting in the Himalayan region: Insights from Aglar and Paligaad rivers

• DOI: 10.1016/j.rineng.2024.102044
• Publication Date: 2024-03-26T16:01:34Z
• Authors: Vikram Kumar; Selim Unal; Suraj Kumar Bhagat; Tiyasha Tiyasha
• Keywords: Exponential Smoothing; Discharge

This study aims to better understand the time series forecasting of Aglar and Paligaad rivers' discharge (which has a significant impact on Himalayan river) using advanced methods such as Holt-Winters (HW) additive method, Simple exponential smoothing (SES), Non-seasonal ARIMA models. used antecedent information forecast next event. Comprehensive statistical examinations were conducted analyzed. The highly stochastic nature these river trends adds complexity efforts requires sophisticated modeling techniques that are capable capturing interpreting variability accurately. models proposed in current provide reliable for 15 months 31 recorded data. analysis shows both HW non-seasonal model results indicate decay end 2016 early 2017. best performance long-term forecasting, indicating sharp increase spring small during fall months. However, short-term non-ARIMA should show more promising results. methodologies substantially improve accuracy all consecutive perennial rivers. While presents discharge, generalizing findings other systems or different geographical regions may be problematic due varying hydrological characteristics environmental conditions, which need further study.