Article: Long-range forecasting of daily rainfall using machine learning techniques Journal: International Journal of Hydrology Science and Technology (IJHST) 2025 Vol.19 No.2 pp.117 - 151 Abstract: Around the world, awareness of the value of rainfall forecasting and its social and economic advantages is rapidly growing. Nation like India, where agriculture is one of the main economic drivers, accurate rainfall forecasting is essential for managing water resources and reducing hydrological extremes. To predict daily rainfall for a year in the drought-prone Kangsabati river basin, long short-term memory (LSTM) and random forest (RF) techniques were utilised using the data of 52 years (1969 to 2020). Finding the right associated variable and a substantial lag in the time series that allows for future value prediction is essential when developing time series forecasting models. The partial auto correlation function and Pearson correlation technique were applied in this context. Furthermore, comparisons were made between the suggested models and the well-known statistical model, the seasonal auto-regressive integrative moving average (SARIMA). The study shows that for this region, the proposed LSTM model and SARIMA model had higher accuracy than the RF model. Additionally, this research suggests that machine learning algorithms may be used to analyse daily rainfall for a particular catchment or station and, to a certain extent, forecast extreme hydrological events. Inderscience Publishers - linking academia, business and industry through research

Title: Long-range forecasting of daily rainfall using machine learning techniques

Authors: Syamantak Bhunia; Ujjwal Saha

Addresses: Civil Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, India ' Faculty of Civil Engineering Department, Indian Institute of Engineering Science and Technology, Shibpur, India

Abstract: Around the world, awareness of the value of rainfall forecasting and its social and economic advantages is rapidly growing. Nation like India, where agriculture is one of the main economic drivers, accurate rainfall forecasting is essential for managing water resources and reducing hydrological extremes. To predict daily rainfall for a year in the drought-prone Kangsabati river basin, long short-term memory (LSTM) and random forest (RF) techniques were utilised using the data of 52 years (1969 to 2020). Finding the right associated variable and a substantial lag in the time series that allows for future value prediction is essential when developing time series forecasting models. The partial auto correlation function and Pearson correlation technique were applied in this context. Furthermore, comparisons were made between the suggested models and the well-known statistical model, the seasonal auto-regressive integrative moving average (SARIMA). The study shows that for this region, the proposed LSTM model and SARIMA model had higher accuracy than the RF model. Additionally, this research suggests that machine learning algorithms may be used to analyse daily rainfall for a particular catchment or station and, to a certain extent, forecast extreme hydrological events.

Keywords: hyperparameter; long short-term memory; LSTM; machine learning; rainfall forecasting; RF; SARIMA.

DOI: 10.1504/IJHST.2025.144237

International Journal of Hydrology Science and Technology, 2025 Vol.19 No.2, pp.117 - 151

Received: 27 Mar 2023
Accepted: 19 Nov 2023
Published online: 03 Feb 2025 *

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Title: Long-range forecasting of daily rainfall using machine learning techniques

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