Application of artificial neural network with optimization of genetic algorithms for weather prediction
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Published:
May 30, 2024
Keywords:
Artificial Neural Networks, Genetic Algorithms, Optimization, Prediction Accuracy, Weather Prediction
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Computer Science
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Abstract
This research integrates Artificial Neural Network (ANN) with Genetic Algorithm Optimization (GA) to improve the accuracy of weather prediction. This method utilizes ANN-optimized GA, creating a model that can adapt to the dynamics of weather patterns. Using a dataset that includes meteorological variables such as temperature, humidity, and precipitation from January 1, 2023, to October 28, 2023, the model was tested for its ability to predict weather conditions accurately. The process begins with data preprocessing, ANN training, and GA optimisation. The evaluation showed that the optimized model was able to reduce the Mean Absolute Error (MAE) from 1.6865 to 0.8701, the Mean Absolute Percentage Error (MAPE) from 5.9864 to 3.1408, and the Root Mean Squared Error (RMSE) from 2.253 to 1.039, signalling a significant improvement in prediction accuracy and efficiency. This research confirms the potential of ANN and GA integration in improving weather prediction, providing new insights for developing more accurate and reliable prediction models for various applications, from agriculture to disaster management.
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How to Cite
Gunawan, G. ., Miftakhudin, M. . and Arif, Z. . (2024) “Application of artificial neural network with optimization of genetic algorithms for weather prediction”, Jurnal Mantik, 8(1), pp. 758-767. doi: 10.35335/mantik.v8i1.5225.
References
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?alasan, M., Aleem, S. H. E. A., & Zobaa, A. F. (2020). On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: A novel exact analytical solution based on Lambert W function. Energy Conversion and Management, 210, 112716. https://doi.org/https://doi.org/10.1016/j.enconman.2020.112716
Donadio, L., Fang, J., & Porté-Agel, F. (2021). Numerical weather prediction and artificial neural network coupling for wind energy forecast. Energies, 14(2), 338. https://doi.org/https://doi.org/10.3390/en14020338
Fathi, M., Haghi Kashani, M., Jameii, S. M., & Mahdipour, E. (2022). Big data analytics in weather forecasting: A systematic review. Archives of Computational Methods in Engineering, 29(2), 1247–1275. https://doi.org/https://doi.org/10.1007/s11831-021-09630-6
Ghil, M., & Lucarini, V. (2020). The physics of climate variability and climate change. Reviews of Modern Physics, 92(3), 35002. https://doi.org/https://doi.org/10.1103/RevModPhys.92.035002
Guo, H., Nguyen, H., Bui, X.-N., & Armaghani, D. J. (2021). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers, 37(1), 421–435. https://doi.org/https://doi.org/10.1007/s00366-019-00833-x
Hamdia, K. M., Zhuang, X., & Rabczuk, T. (2021). An efficient optimization approach for designing machine learning models based on genetic algorithm. Neural Computing and Applications, 33(6), 1923–1933. https://doi.org/https://doi.org/10.1007/s00521-020-05035-x
Ilbeigi, M., Ghomeishi, M., & Dehghanbanadaki, A. (2020). Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm. Sustainable Cities and Society, 61, 102325. https://doi.org/https://doi.org/10.1016/j.scs.2020.102325
Khan, P. W., & Byun, Y.-C. (2020). Genetic algorithm based optimized feature engineering and hybrid machine learning for effective energy consumption prediction. Ieee Access, 8, 196274–196286. https://doi.org/10.1109/ACCESS.2020.3034101
Li, Y., Jia, M., Han, X., & Bai, X.-S. (2021). Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA). Energy, 225, 120331. https://doi.org/https://doi.org/10.1016/j.energy.2021.120331
Liang, Y., Yu, C., & Ma, W. (2021). The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life. PLoS Computational Biology, 17(12), e1009761. https://doi.org/https://doi.org/10.1371/journal.pcbi.1009761
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Owolabi, H. A., & Ahmed, A. (2020). Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renewable and Sustainable Energy Reviews, 131, 109980. https://doi.org/https://doi.org/10.1016/j.rser.2020.109980
Muhmad Kamarulzaman, A. M., Wan Mohd Jaafar, W. S., Mohd Said, M. N., Saad, S. N. M., & Mohan, M. (2023). UAV Implementations in Urban Planning and Related Sectors of Rapidly Developing Nations: A Review and Future Perspectives for Malaysia. Remote Sensing, 15(11), 2845. https://doi.org/https://doi.org/10.3390/rs15112845
Nazir, M. S., Alturise, F., Alshmrany, S., Nazir, H. M. J., Bilal, M., Abdalla, A. N., Sanjeevikumar, P., & M. Ali, Z. (2020). Wind generation forecasting methods and proliferation of artificial neural network: A review of five years research trend. Sustainability, 12(9), 3778. https://doi.org/https://doi.org/10.3390/su12093778
Qi, J., Du, J., Siniscalchi, S. M., Ma, X., & Lee, C.-H. (2020). On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters, 27, 1485–1489. https://doi.org/10.1109/LSP.2020.3016837
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020). WeatherBench: a benchmark data set for data?driven weather forecasting. Journal of Advances in Modeling Earth Systems, 12(11), e2020MS002203. https://doi.org/https://doi.org/10.1029/2020MS002203
Sadeghi-Niaraki, A., Mirshafiei, P., Shakeri, M., & Choi, S.-M. (2020). Short-term traffic flow prediction using the modified elman recurrent neural network optimized through a genetic algorithm. IEEE Access, 8, 217526–217540. https://doi.org/10.1109/ACCESS.2020.3039410
Sun, G., Li, C., & Deng, L. (2021). An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Computing and Applications, 33, 9503–9519. https://doi.org/https://doi.org/10.1007/s00521-021-05708-1
Terzi, S., Torresan, S., Schneiderbauer, S., Critto, A., Zebisch, M., & Marcomini, A. (2019). Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation. Journal of Environmental Management, 232, 759–771. https://doi.org/https://doi.org/10.1016/j.jenvman.2018.11.100
Toharudin, T., Caraka, R. E., Pratiwi, I. R., Kim, Y., Gio, P. U., Sakti, A. D., Noh, M., Nugraha, F. A. L., Pontoh, R. S., & Putri, T. H. (2023). Boosting Algorithm to handle Unbalanced Classification of PM2. 5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia using AdaBoost, XGBoost, CatBoost, and LightGBM. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3265019
Uccellini, L. W., & Ten Hoeve, J. E. (2019). Evolving the National Weather Service to build a weather-ready nation: Connecting observations, forecasts, and warnings to decision-makers through impact-based decision support services. Bulletin of the American Meteorological Society, 100(10), 1923–1942. https://doi.org/https://doi.org/10.1175/BAMS-D-18-0159.1
Vasseghian, Y., Arunkumar, P., Joo, S.-W., Gnanasekaran, L., Kamyab, H., Rajendran, S., Balakrishnan, D., Chelliapan, S., & Klemeš, J. J. (2022). Metal-organic framework-enabled pesticides are an emerging tool for sustainable cleaner production and environmental hazard reduction. Journal of Cleaner Production, 373, 133966. https://doi.org/https://doi.org/10.1016/j.jclepro.2022.133966
Viana, M. S., Morandin Junior, O., & Contreras, R. C. (2020). A modified genetic algorithm with local search strategies and multi-crossover operator for job shop scheduling problem. Sensors, 20(18), 5440. https://doi.org/https://doi.org/10.3390/s20185440
Viet, D. T., Phuong, V. Van, Duong, M. Q., & Tran, Q. T. (2020). Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms. Energies, 13(11), 2873. https://doi.org/https://doi.org/10.3390/en13112873
Xu, C., Coen-Pirani, P., & Jiang, X. (2023). Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis. Cancers, 15(7), 1969. https://doi.org/https://doi.org/10.3390/cancers15071969
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