Volume 6 • Issue 2 • PP: 64-71 • 2021
Weather Forecasting for Batu Pahat Using Neural Network
Open Access & Copyright
© 2021 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Abstract
Nowadays, weather forecasting plays a vital role in human activities. The complexity of data gathering in the meteorology department and high technical costs lead to inaccurate weather forecasting. Due to minimize this problem, an application using an artificial neural network (ANN) has been developed to forecast weather conditions using the Matlab compiler. This application will help users to define the weather conditions daily well and make early preparation to encounter uncertainty. This application performs in Multilayer Perceptron (MLP) using the back-propagation (BP) algorithm. The data is undergoing training and testing based on actual data obtained from the Malaysian Meteorological Department. The result will show in graph plotting for the training and testing process. Predictive accuracy for each step of stimulation will measure using Mean Square Error (MSE) graph in this application. Therefore, this application will be able to assist the Malaysian Meteorological Department to forecast weather, so our government and people have sufficient time to prepare and solve.
Keywords
References
[1] Hamedianfar, A., & Shafri, H. Z. M. (2016). Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data. Journal of applied remote sensing, 10(2), 025001.
[2] Hassan, M. H., Mostafa, S. A., Mohammed, M. A., Ibrahim, D. A., Khalaf, B. A., & Al-Khaleefa, A. S. (2019). Integrating African Buffalo optimization algorithm in AODV routing protocol for improving the QoS of MANET. Journal of Southwest Jiaotong University, 54(3).
[3] Khalaf, B. A., Mostafa, S. A., Mustapha, A., & Abdullah, N. (2018, August). An adaptive model for detection and prevention of DDoS and flash crowd flooding attacks. In 2018 International Symposium on Agent, Multi-Agent Systems and Robotics (ISAMSR) (pp. 1-6). IEEE.
[4] Khalaf, B. A., Mostafa, S. A., Mustapha, A., Ismaila, A., Mahmoud, M. A., Jubaira, M. A., & Hassan, M. H. (2019). A simulation study of syn flood attack in cloud computing environment. AUS journal, 26(1), 188-197.
[5] Met (2012). Official Portal Malaysian Meteorological Department. Retrieved October 20, 2012, from http://www.met.gov.my
[6] Abdalmunam, A., Anuar, M. S., Junta, M. N., Nawawi, N. M., & Noori, A. (2020, February). Implementation of Particle Swarm Optimization and Genetic Algorithms to Tackle the PAPR Problem of OFDM System. In IOP Conference Series: Materials Science and Engineering (Vol. 767, No. 1, p. 012030). IOP Publishing.
[7] Olaiya, F. (2012). Application of Data Mining Techniques in Weather Prediction and Climatic Change Studies. I.J. Information Engineering and Electronic Business, 2012, 1, p.51-59.
[9] Padhy, N.P. (2005). Artificial Intelligence and Intelligence Systems. New Delhi: Oxford University Press.
[10] Tektas, M. (2010). Weather Forecasting Using ANFIS and ARIMA MODELS. A Case Study for Istanbul. Environmental Research, Engineering and Management, 2010, 1(51), p.5-10.
[11] Wikipedia (2012). Weather Forecasting. Retrieved on October 3, 2012, from http://en.wikipedia.org/wiki/Weather_forecasting.
[12] Ghani, M. K. A., Mohammed, M. A., Ibrahim, M. S., Mostafa, S. A., & IBRAHIM, D. A. (2017). Implementing an Efficient Expert System For Services Center Management By Fuzzy Logic Controller. Journal of Theoretical & Applied Information Technology, 95(13).
[13] Mostafa, S. A., Ahmad, M. S., Mohammed, M. A., & Obaid, O. I. (2012). Implementing an expert diagnostic assistance system for car failure and malfunction. International Journal of Computer Science Issues (IJCSI), 9(2), 1.
[14] Mostafa, S. A., Mustapha, A., Hazeem, A. A., Khaleefah, S. H., & Mohammed, M. A. (2018). An agent-based inference engine for efficient and reliable automated car failure diagnosis assistance. IEEE Access, 6, 8322-8331.
[15] Zheyuan, C., Hammid, A. T., Kareem, A. N., Jiang, M., Mohammed, M. N., & Kumar, N. M. (2021). A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem. Sustainability, 13(8), 4277.
[16] SHUJAA, M. (2019). LAGGED MULTI-OBJECTIVE JUMPING PARTICLE SWARM OPTIMIZATION FOR WIRELESS SENSOR NETWORK DEPLOYMENT. Journal of Theoretical and Applied Information Technology, 97(2).
[17] Hammadi, Y. I., Mansour, T. S., Al-Masoodi, A. H. H., & Harun, S. W. (2019). Passively Femtosecond Mode-Locked Erbium-Doped Fiber Oscillator with External Pulse Compressor for Frequency Comb Generation. Journal of Optical Communications.
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