پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 312 |
| تعداد مقالات | 3,314 |
| تعداد مشاهده مقاله | 3,531,214 |
| تعداد دریافت فایل اصل مقاله | 2,583,981 |
Rainfall Prediction in Khorasan Razavi Stations Using a Hybrid Neural Network and Genetic Algorithm Approach | ||
| Water Harvesting Research | ||
| دوره 8، شماره 1، خرداد 2025، صفحه 43-57 اصل مقاله (1.46 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2025.8988.1169 | ||
| نویسندگان | ||
| Mahdi Naseri* 1؛ Mahsa Mardani2 | ||
| 1Assistant Professor, Faculty of Engineering, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| 2Ph.D. Student, Faculty of Engineering, Department of Civil Engineering, University of Birjand, Birjand, Iran. | ||
| چکیده | ||
| Accurate rainfall prediction is crucial for effective water resource management, especially in arid and semi-arid regions. This study proposes a novel hybrid approach, combining the Non-linear Auto Regressive with eXogenous inputs (NARX) neural network with a Genetic Algorithm (GA) for parameter optimization, aiming to improve daily rainfall prediction in Khorasan Razavi province, Iran. The performance of the proposed NARXGA model was compared with several benchmark models, including traditional time series models ARIMA, Holt-Winters Exponential Smoothing (HWES), and machine learning models, such as LSTM, CNN1D and the standalone NARX network. The models were trained and tested using five years of daily meteorological data from Mashhad. The results showed that the NARXGA model achieved the lowest Mean Squared Error (MSE) on both the training and test datasets, with values of 9.7453 and 11.5565, respectively, thus showing that the method can more effectively capture the non-linear patterns in rainfall data. A convergence analysis of the GA was also provided, as well as histograms of the error distributions, which further validated the superior performance of the proposed NARXGA model. This research highlights the potential of hybrid AI models for enhancing rainfall prediction accuracy and providing valuable insights for water management and drought mitigation in arid and semi-arid regions. | ||
| کلیدواژهها | ||
| Genetic Algorithm؛ Hybrid Model؛ Khorasan Razavi Province؛ NARX Neural Network؛ Rainfall Prediction | ||
| مراجع | ||
|
Akbari Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., & Peng, Q. (2018). Short‐term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks. Journal of Geophysical Research: Atmospheres, 123(22), 12,543-512,563.
Aoulmi, Y., Marouf, N., Rasouli, K., & Panahi, M. (2023). Runoff predictions in a semiarid watershed by convolutional neural networks improved with metaheuristic algorithms and forced with reanalysis and climate data. Journal of Hydrologic Engineering, 28(7), 04023018.
Baig, F., Ali, L., Faiz, M. A., Chen, H., & Sherif, M. (2024). How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment? Journal of Hydrology, 633, 131040.
Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204.
Bochenek, B., & Ustrnul, Z. (2022). Machine learning in weather prediction and climate analyses—applications and perspectives. Atmosphere, 13(2), 180.
Dotse, S.-Q., Larbi, I., Limantol, A. M., & De Silva, L. C. (2024). A review of the application of hybrid machine learning models to improve rainfall prediction. Modeling Earth Systems and Environment, 10(1), 19-44.
El Shafie, A. H., El-Shafie, A., Almukhtar, A., Taha, M. R., El Mazoghi, H. G., & Shehata, A. (2012). Radial basis function neural networks for reliably forecasting rainfall. Journal of water and climate change, 3(2), 125-138.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimisation and Machine Learning (1 ed.). Addison-Wesley.
Hochreiter, S., #252, & Schmidhuber, r. (1997). Long Short-Term Memory. Neural Comput., 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kaveh, M., & Mesgari, M. S. (2023). Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review. Neural Processing Letters, 55(4), 4519-4622.
Lai, L., Li, C., & Zhang, Y. (2024). Research on Rainfall Prediction Based on GA-SVM Model. 2024 4th International Symposium on Computer Technology and Information Science (ISCTIS),
Le, V. M., Pham, B. T., Le, T.-T., Ly, H.-B., & Le, L. M. (2020). Daily rainfall prediction using nonlinear autoregressive neural network. Micro-Electronics and Telecommunication Engineering: Proceedings of 3rd ICMETE 2019,
Lima, S., Gonçalves, A. M., & Costa, M. (2019). Time series forecasting using Holt-Winters exponential smoothing: An application to economic data. AIP conference proceedings,
Nelson, B. K. (1998). Time series analysis using autoregressive integrated moving average (ARIMA) models. Academic emergency medicine, 5(7), 739-744.
Nemade, B., Mishra, R., Jangid, P., Dubal, S., Bharadi, V., & Kaul, V. (2023). Improving Rainfall Prediction Accuracy Using an LSTM-Driven Model Enhanced by M-PSO Optimization. Journal of Electrical Systems, 19(3).
Ngan, T. T., Son, H. G., Omar, M., Thang, N. T., Giang, N. L., Tuan, T. M., & Tho, N. A. (2023). A hybrid of RainNet and genetic algorithm in nowcasting prediction. Earth Science Informatics, 16(4), 3885-3894.
Pham, B. T., Bui, K.-T. T., Prakash, I., & Ly, H.-B. (2024). Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall. Physics and Chemistry of the Earth, Parts A/B/C, 134, 103563.
Ponnoprat, D. (2021). Short-term daily precipitation forecasting with seasonally-integrated autoencoder. Applied Soft Computing, 102, 107083.
Poornima, S., & Pushpalatha, M. (2019). Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units. Atmosphere, 10(11), 668.
Priatna, M. A., & Djamal, E. C. (2020). Precipitation prediction using recurrent neural networks and long short-term memory. Telkomnika (Telecommunication Computing Electronics and Control), 18(5), 2525-2532.
Pujara, M., & Paudel, N. (2024). Rainfall Prediction using Long Short-Term Memory and Gated Recurrent Unit with Various Meteorological Parameters. Nepalese Journal of Statistics, 8, 47-60.
Salehinejad, H., Sankar, S., Barfett, J., Colak, E., & Valaee, S. (2017). Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078.
Sun, W., Su, F., & Wang, L. (2018). Improving deep neural networks with multi-layer maxout networks and a novel initialization method. Neurocomputing, 278, 34-40.
Wei, M., & You, X.-y. (2022). Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transformation and deep learning. Water Resources Management, 36(11), 4003-4018.
Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23), 495.
Wu, J., Long, J., & Liu, M. (2015). Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing, 148, 136-142.
Xiang, Y., Gou, L., He, L., Xia, S., & Wang, W. (2018). A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874-883.
Y.LeCun, Y.Bengio, & G.Hinton. (2015). Deep learning. Nature, 521(7553), 436–444.
Zabihi, O., Gheibi, M., Aghlmand, R., & Nejatianc, A. (2022). Scrutinizing famine disaster based on rainfall trend investigation (a case study of Khorasan Razavi province). Journal of Water, 1(2), 17-26. | ||
|
آمار تعداد مشاهده مقاله: 57 تعداد دریافت فایل اصل مقاله: 33 |
||
نماد الکترونیک
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب