Volume 2 • Issue 2 • PP: 39-45 • 2022
Weather Forecasting over Iraq Using Machine Learning
Open Access & Copyright
© 2022 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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The weather generally comprises various factors, such as wind speed, precipitation, and rainfall. Environmental weather forecasting is a demanding task for researchers, and in recent years it has attracted much study attention. Our assessment considers a wide range of weather conditions across Iraq utilizing information gathered from NASA's estimate of the world's energy resources for the years 1981 to 2021. Therefore, the correct forecast of meteorological parameters is a difficult challenge due to their changing environmental conditions. Random forest, decision tree, and GBR algorithms are used for weather forecasting. A comparison among used methods is performed and the RF is achieved the best results with accuracy, MAE, MSE, R2 of 92%, 0.5, 2.45, and 0.92, respectively.
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References
[1] K. Stan et al., Climate change scenarios and projected impacts for forest productivity in Guanacaste Province (Costa Rica): lessons for tropical forest regions. Reg. Environ. Chang., 20(14), 1–13, 2020.
[2] C. Harkness et al., Adverse weather conditions for UK wheat production under climate change. Agric For Meteorol, 15, 282–283, 2020.
[3] Z. Li, Q. Li, J. Wang, Y. Feng, and Q. Shao, Impacts of projected climate change on runoff in upper reach of Heihe River basin using climate elasticity method and GCMs. Sci. Total Environ., 716, 137072, 2020.
[4] B. Bochenek and Z. Ustrnu, Machine Learning in Weather Prediction and Climate Analyses — Applications and Perspectives. Atmosphere (Basel)., 13(180), 1-16, 2022.
[5] G. M. Dharsan, A survey on weather forecasting and their techniques. Int. Res. J. Mod. Eng. Technol. Sci., 4(2), 12–17, 2022.
[6] N. A. Al-hammadi, ESTIMATION OF MEAN AREAL RAINFALL AND MISSING DATA BY USING GIS IN NINEVEH, NORTHERN IRAQ. Iraqi Geol. J., 53, 93–103, 2020.
[7] H. Astsatryan, H. Grigoryan, A. Poghosyan, R. Abrahamyan, and S. Asmaryan, Air temperature forecasting using artificial neural network for Ararat valley. Earth Sci. Inform., 14(2), 711–722, 2021.
[8] Z. Zhang and Y. Dong,Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time-Series Data.Complexity, 2020.
[9] T. S. Khayyun, I. A. Alwan, and A. M. Hayder, Selection of Suitable Precipitation CMIP-5 Sets of GCMs for Iraq Using a Symmetrical Uncertainty Filter. IOP Conf. Ser. Mater. Sci. Eng., 671(1), 2020.
[10] F. K. Bishay, Towards sustainable agricultural development in Iraq. The transition from relief, rehabilitation and reconstruction to development. FAO, 200, 2003.
[11] Mohamed Saber, A novel design and implementation of FBMC transceiver for low power applications. IJEEI, 8(1), 83-93, 2020.
[12] Mohamed Saber, Efficient phase recovery system. IJEECS, 5(1), 123-129,2017.
[13] C. M. Bishop, Pattern recognition and machine learning, vol. 4, no. 4. Springer New York, 2006.
[14] A. Keprate and R. M. C. Ratnayake, Using Gradient Boosting Regressor to Predict Stress Intensity Factor of a Crack Propagating in Small Bore Piping. in 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1331-1336, 2017.
[15] D. Wolfensberger, M. Gabella, M. Boscacci, U. Germann, and A. Berne, RainForest: A random forest algorithm for quantitative precipitation estimation over Swizerland. Atmos. Meas. Tech. Discuss., 14, 1–35, 2020.
[16] N. Sirikulviriya and S. Sinthupinyo, Integration of Rules from a Random Forest Int. Conf. Inf. Electron. Eng., 6, 194–198, 2011.
[17] Doaa Sami Khafaga Hussah Nasser AlEisa, El-Sayed M. El-kenawy, Amel Ali Alhussan, Mohamed Saber, Abdelaziz A. Abdelhamid, Transfer learning for chest X-rays diagnosis using dipper throated algorithm, CMC, 73(2), 2371-2387, 2022.
[18] Marwa M. Eid, Fawaz Alassery, Abdelhameed Ibrahim, Mohamed Saber, Metaheuristic optimization algorithm for signal classification of electroencephalography channels. CMC, 71(3), 4627-4641, 2022.
[19] E. Saloux and J. A. Candanedo, Forecasting District Heating Demand using Machine Learning. Energy Procedia, 149, 59–68, 2018.
[20] N. A. Saeed and Z. T. M. Al-Ta’i, Heart Disease Prediction System Using Optimization Technique. in New Trends in Information and Communications Technology Applications, 167–177, 2020.
[21] M. Gholami Rostam, S. J. Sadatinejad, and A. Malekian, “Precipitation forecasting by large-scale climate indices and machine learning techniques,” J. Arid Land, vol. 12, no. 5, pp. 854–864, 2020.
[22] D. Chicco, M. J. Warrens, and G. Jurman, “The coef fi cient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., pp. 1–24, 2021.
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