Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Abstract

Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

“…The findings indicated that these factors reasonably estimated the observed rainfall better than individual factors, implying that considering the synergistic effects of these influencing factors is important in interpreting and predicting the SON rainfall variations over SESA. Similar findings were presented by Rasel et al (2016), Khastagir et al (2022), Imteaz (2012), andWei et al (2023), but they focused on South, West, Victoria Australian rainfall and Arabian Peninsula rainfall, respectively. The authors demonstrated that rainfall predictability significantly enhanced based on the joint rainfall predictors relative to their separate Journal of Geophysical Research: Atmospheres 10.1029/2023JD040309 considerations.…”

“…(2016), Khastagir et al. (2022), Mekanik and Imteaz (2012), and Wei et al. (2023), but they focused on South, West, Victoria Australian rainfall and Arabian Peninsula rainfall, respectively.…”

Precipitation forecasting: from geophysical aspects to machine learning applications

“…Some recently published works have applied and investigated how different ML algorithms and meteorological variables can contribute to precipitation forecasting in many regions worldwide, using various time scales according to the socioeconomic necessities of each region. Regarding the seasonal time scale, Khastagir et al (2022) evaluated the efficacy of predicting precipitation over Western Australia, which is indispensable for flood mitigation as well as water resource management for that region. The authors used ANN and multiple linear regression (MLR) analysis to forecast long-term seasonal spring precipitation using lagged El Niño-Southern Oscillation and Indian Ocean Dipole as potential climatic phenomena.…”

Comparison of Future Design Rainfall with Current Design Rainfall: A Case Study in New South Wales, Australia

“…In addition to climate change impacts on rainfall (and design rainfall), Australian rainfall is extremely manipulated by several climate drivers including El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annual Mode (SAM) [21][22][23][24]. Due to the natural variability and changes in the large-scale climatic modes from excessive greenhouse gas emissions, rainfall in the southeast area of Australia has decreased over the last 20 years [25].…”