Open Access

ARTICLE

Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

by El-Sayed M. El-kenawy¹, Abdelaziz A. Abdelhamid^2,3, Fadwa Alrowais^4,*, Mostafa Abotaleb⁵, Abdelhameed Ibrahim⁶, Doaa Sami Khafaga⁴

1 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
2 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
3 Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Saudi Arabia
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of System Programming, South Ural State University, Chelyabinsk, 454080, Russia
6 Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

* Corresponding Author: Fadwa Alrowais. Email:

Computer Systems Science and Engineering 2023, 45(3), 2885-2899. https://doi.org/10.32604/csse.2023.034206

Received 09 July 2022; Accepted 12 August 2022; Issue published 21 December 2022

Abstract

Rainfall plays a significant role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. Many individuals, especially those in the agricultural sector, rely on rain forecasts. Forecasting rainfall is challenging because of the changing nature of the weather. The area of Jimma in southwest Oromia, Ethiopia is the subject of this research, which aims to develop a rainfall forecasting model. To estimate Jimma’s daily rainfall, we propose a novel approach based on optimizing the parameters of long short-term memory (LSTM) using Al-Biruni earth radius (BER) optimization algorithm for boosting the forecasting accuracy. Nash–Sutcliffe model efficiency (NSE), mean square error (MSE), root MSE (RMSE), mean absolute error (MAE), and R² were all used in the conducted experiments to assess the proposed approach, with final scores of (0.61), (430.81), (19.12), and (11.09), respectively. Moreover, we compared the proposed model to current machine-learning regression models; such as non-optimized LSTM, bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM). It was found that the proposed approach achieved the lowest RMSE of (19.12). In addition, the experimental results show that the proposed model has R² with a value outperforming the other models, which confirms the superiority of the proposed approach. On the other hand, a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected performance.

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