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.
Purpose: To evaluate the superpave design performance using Epolene (EE-2) as modifier, since SUPERPAVE design is a modified and sophisticated aspect as compared to previous mix design for asphalt mixtures. This is primarily due to the fact that superpave design mix also takes into consideration properties of materials beside asphalt. Design/methodology/approach: This study was conducted using Epolene (EE-2) as modifier in order to evaluate the performance of SUPERPAVE suitability for construction of roads in Alfaraa campus (King Khalid University) Abha, in Asir Province of Saudi Arabia. Glow number test, dynamic modulus test and indirect tensile strength test were conducted to evaluate the performance of EE-2 modifier against the control mixture. Findings: The mixture modified with EE-2 gave better performance in terms of temperature-based performance and resistance to moisture damage. Also, larger values of E*/sinφ were obtained for EE-2 modified mixture at various loading frequencies and temperature in comparison to control mixture. Research limitations/implications: The Epolene modifier successfully enhances and improves the SUPERPAVE mixture performance. Further studies are required to evaluate the performance of EE-2 modifier at much lower temperature ranges. Practical implications: The results of the study allow us to recommend the investigated asphalt mixture for applied for the construction of roads in the Alfaraa (new campus of King Khalid University), Abha, Asir province, Saudi Arabia. Originality/value: A modified asphalt mixture has been proposed that has better performance at higher and lower temperatures. The developed asphalt mixture is more resistant to moisture damage than the compared to control mixture.
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