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

Abstract

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

Keywords

• Time series analysis
• Holt-winters (HW) additive
• Non-seasonal ARIMA
• Paligaad
• Simple exponential smoothing (SES)
• River discharge