پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 643 |
| تعداد مقالات | 9,418 |
| تعداد مشاهده مقاله | 68,154,649 |
| تعداد دریافت فایل اصل مقاله | 42,197,985 |
Deep learning for big weather data analyzing and forecasting | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 8، دوره 15، شماره 2، اردیبهشت 2024، صفحه 87-94 اصل مقاله (521.36 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30013.4313 | ||
| نویسندگان | ||
| Delveen L. Abd Al-Nabi* 1؛ Shereen Sh. Ahmed2 | ||
| 1Department of Economics, College of Economics and Administration, Duhok University, Duhok, Kurdistan Region, Iraq | ||
| 2Department of Computer Science, Faculty of Science, Zakho University, Duhok, Kurdistan Region, Iraq | ||
| تاریخ دریافت: 14 دی 1401، تاریخ بازنگری: 01 اسفند 1401، تاریخ پذیرش: 20 اسفند 1401 | ||
| چکیده | ||
| Weather prediction is vital in daily life routines, for risk mitigation and resource management such as flood risk forecasting. Quantitative prediction of weather changes depends on different parameters such as rainfall time, temporal, barometric pressure, humidity, precipitation, solar radiation and wind. Therefore, a highly accurate system or a model to forecast the highly nonlinear changing happening in the climate is required. The focus of this research is direct prediction of forecasting from weather-changing parameters, the forecasts are performed using collected data values recorded in a big dataset (the dataset collects the weather parameter data of the Canary Islands (Las Palmas, Tenerife a Palma, Fuerteventura, La Gomera, Lanzarote and Hierro). The forecasting system is performed by proposing a deep learning approach (CNN). The research goal is predication the weather condition. The acquired classification accuracy for the climate condition using CNN (ShuffleNet) structure is 98%, and the recall and Precision results are 97.5 and 96.9 respectively | ||
| کلیدواژه‌ها | ||
| Deep learning؛ big data analysis؛ weather predication؛ machine learning | ||
| مراجع | ||
|
[1] A. Aichert, Feature extraction techniques, Camp. Med. Seminar Ws0708, 2008, pp. 1–8. [2] M. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, Disease prediction by machine learning over big data from healthcare communities, IEEE Access 5 (2017), 8869–8879. [3] X.-W. Chen and X. Lin, Big data deep learning: Challenges and perspectives, IEEE Access 2 (2014), 514–525. [4] L. Deng and D. Yu, Deep learning: Methods and applications, Found. Trends Signal Process. 7 (2014), no. 3-4, 197–387. [5] L. Deng, D. Yu, and J. Platt, Scalable stacking and learning for building deep architectures, IEEE Int. Conf. Acoustics Speech Signal Process. (ICASSP), IEEE, 2012, pp. 2133–2136. [6] C.J. Devi, B.S. Prasad Reddy, K.V. Kumar, B.M. Reddy, and N.R. Nayak, Ann approach for weather prediction using backpropagation, Int. J. Engin. Trends Technol. 3 (2012), no. 1, 19–23. [7] A. Efrati, How “deep learning” works at Apple, beyond, https://www.theinformation.com/articles/how-deep-learning-works-at-apple-beyond, 2017. [8] M. Elhoseiny, S. Huang, and A. Elgammal, Weather classification with deep convolutional neural networks, IEEE Int. Conf. Image Process. (ICIP), IEEE, 2015, pp. 3349–3353. [9] H. Gholamalinezhad and H. Khosravi, Pooling methods in deep neural networks, a review, ArXiv Preprint ArXiv:2009.07485 (2020). [10] A.B. Hernandez, M.S. Perez, S. Gupta, and V. Muntes-Mulero, Using machine learning to optimize parallelism in big data applications, Future Gen. Comput. Syst. 86 (2018), 1076–1092. [11] B. Hutchinson, L. Deng, and D. Yu, Tensor deep stacking networks, IEEE Trans. Pattern Anal. Machine Intell. 35 (2012), no. 8, 1944–1957. [12] N. Jones, The learning machines, Nature 505 (2014), no. 7482, 146. [13] A. Krizhevsky, I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol. 25, 2012. [14] Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature 521 (2015), no. 7553, 436–444. [15] C. Ma, H.H. Zhang, and X. Wang, Machine learning for big data analytics in plants, Trends Plant Sci. 19 (2014), no. 12, 798–808. [16] A. Mathew, P. Amudha, and S. Sivakumari, Deep learning techniques: an overview, Int. Conf. Adv. Machine Learn. Technol. Appl., 2020, pp. 599–608. [17] R. Raina, A. Madhavan, and A.Y. Ng, Large-scale deep unsupervised learning using graphics processors, Proc. 26th Ann. Int. Conf. Machine Learn., 2009, pp. 873–880. [18] Sebastian Scher and Gabriele Messori, Weather and climate forecasting with neural networks: Using general circulation models (GCMs) with different complexity as a study ground, Geosci. Model Dev. 12 (2019), no. 7, 2797–2809. [19] W. Scott, TF-IDF for Document Ranking from scratch in python on real world dataset, https://towardsdatascience.com/tf-idf-for-document-ranking-from-scratch-in-python-on-real-world-dataset-796d339a4089, 2020. [20] A. Subashini, S.M. Thamarai, and T. Meyyappan, Advanced weather forecasting prediction using deep learning, Int. J. Res. Appl. Sci. Engin. Technol. 7 (2019), 939–945. [21] F. Sun, A. Belatreche, S. Coleman, T.M. McGinnity, and Y. Li, Pre-processing online financial text for sentiment classification: A natural language processing approach, IEEE Conf. Comput. Intell. Financ. Engin. Econ., 2014, pp. 122–129. [22] G. Varoquaux, P.R. Raamana, D.A. Engemann, A. Hoyos-Idrobo, Y. Schwartz, and B. Thirion, Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines, NeuroImage 145 (2017), 166–179. [23] Rikiya Yamashita, Mizuho Nishio, Ron K. Do, and Kaori Togashi, Convolutional neural networks: an overview and application in radiology, Insights Imag. 9 (2018), no. 4, 611–629. [24] X. Zhang, J. Zhao, and Y. LeCun, Character-level convolutional networks for text classification, Adv. Neural Inf. Process. Syst. 28 (2015). | ||
|
آمار تعداد مشاهده مقاله: 18,836 تعداد دریافت فایل اصل مقاله: 9,015 |
||