Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia
DOI:
https://doi.org/10.12775/EQ.2021.038Keywords
moving average method, data pre-processing, mean absolute error, root mean square error, mean absolute scaled error, performance parameterAbstract
Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region.
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