Volume 24 , Issue 2 , PP: 94-107, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Fuad S. Al-Duais 1 * , Shoraim M. H. A. 2 , Amal O. A. Al magdashi 3 , Badr Eldeen A. A. Abouzeed 4
Doi: https://doi.org/10.54216/IJNS.240209
Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models
Tropical Cyclone , Artificial Intelligence , Neutrosophic , Weather Prediction , Harris Hawks Optimization
[1] Meng, F., Yang, K., Yao, Y., Wang, Z. and Song, T., 2023. Tropical cyclone intensity probabilistic forecasting system based on deep learning. International Journal of Intelligent Systems, 2023.
[2] McNeely, T., Khokhlov, P., Dalmasso, N., Wood, K.M. and Lee, A.B., 2023. Structural forecasting for short-term tropical cyclone intensity guidance. Weather and Forecasting, 38(6), pp.985-998.
[3] Meng, F., Yao, Y., Wang, Z., Peng, S., Xu, D. and Song, T., 2023. Probabilistic forecasting of tropical cyclones intensity using machine learning model. Environmental Research Letters, 18(4), p.044042.
[4] Sun, J., Cai, M., Liu, G. and Zhang, D.L., 2024. A Comparative Study of Mathematical Models for the Tropical Cyclone Intensity–Size Relation. Ocean-Land-Atmosphere Research, 3, p.0035.
[5] Wang, C. and Li, X., 2023. Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review. Atmospheric and Oceanic Science Letters, p.100373.
[6] Song, T., Yang, K., Li, X., Peng, S. and Meng, F., 2024. Probabilistic Estimation of Tropical Cyclone Intensity Based on Multi-Source Satellite Remote Sensing Images. Remote Sensing, 16(4), p.606.
[7] Wang, X., Jiang, H. and Guzman, O., 2024. Relating tropical cyclone intensification rate to precipitation and convective features in the inner core. Weather and Forecasting, 39(2), pp.351-368.
[8] Wang, W., Zhang, Z., Cangialosi, J.P., Brennan, M., Cowan, L., Clegg, P., Hosomi, T., Masaaki, I., Das, A.K., Mohapatra, M. and Sharma, M., 2023. A review of recent advances (2018-2021) on tropical cyclone intensity change from operational perspectives, Part 2: Forecasts by Operational Centers. Tropical Cyclone Research and Review.
[9] Fu, R., Hu, H., Wu, N., Liu, Z. and Jin, W., 2024. Spatiotemporal fusion convolutional neural network: tropical cyclone intensity estimation from multisource remote sensing images. Journal of Applied Remote Sensing, 18(1), pp.018501-018501.
[10] Wu, S.N., Soden, B.J. and Alaka Jr, G.J., 2023. The influence of radiation on the prediction of tropical cyclone intensification in a forecast model. Geophysical research letters, 50(2), p.e2022GL099442.
[11] Zhao, Z., Zhang, Z., Tang, P., Wang, X. and Cui, L., 2024. MT-GN: Multi-Task-Learning-Based Graph Residual Network for Tropical Cyclone Intensity Estimation. Remote Sensing, 16(2), p.215.
[12] Kim, J.H., Ham, Y.G., Kim, D., Li, T. and Ma, C., 2024. Improvement in forecasting short-term tropical cyclone intensity change and their rapid intensification using deep learning. Artificial Intelligence for the Earth Systems.
[13] Ma, Z., Yan, Y., Lin, J. and Ma, D., 2024. A Multi-Scale and Multi-Layer Feature Extraction Network with Dual Attention for Tropical Cyclone Intensity Estimation. IEEE Transactions on Geoscience and Remote Sensing.
[14] Kumar, J.S., Venkataraman, V., Meganathan, S. and Krithivasan, K., 2023. Tropical cyclone intensity and track prediction in the bay of Bengal using LSTM-CSO method. IEEE Access.
[15] Tian, W., Lai, L., Niu, X., Zhou, X., Zhang, Y. and Lim Kam Sian, K.T.C., 2023. Estimating tropical cyclone intensity using dynamic balance convolutional neural network from satellite imagery. Journal of Applied Remote Sensing, 17(2), pp.024513-024513.
[16] Ma, D., Wang, L., Fang, S. and Lin, J., 2023. Tropical cyclone intensity prediction by inter-and intra-pattern fusion based on multi-source data. Environmental Research Letters, 18(1), p.014020.
[17] Jiang, W., Hu, G., Wu, T., Liu, L., Kim, B., Xiao, Y. and Duan, Z., 2023. DMANet_KF: tropical cyclone intensity estimation based on deep learning and Kalman filter from multi-spectral infrared images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[18] Argilagos, M.R., Herrera, A.V. and Valdiviezo, W.V., 2022. Evaluation of nutritional education strategies in schools in ecuador using neutrosophic TOPSIS. International Journal of Neutrosophic Science, 18(3), pp.208-217.
[19] Adamu, M., Jiro, A.A., Abdul-Malik, U.T., Danlami, M. and Abdullahi, I.M., 2023. Enhanced Adaptive Threshold Median Filter For Medical Image Filtering. ATBU Journal of Science, Technology and Education, 11(2), pp.217-224.
[20] Chavan, R. and Pete, D., 2024. Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network. Journal of Engineering and Applied Science, 71(1), p.26.
[21] Jose, J. and Judith, J.E., 2024. Unveiling the IoT's dark corners: anomaly detection enhanced by ensemble modelling. Automatika, 65(2), pp.584-596.
[22] https://www.kaggle.com/datasets/sshubam/insat3d-infrared-raw-cyclone-images-20132021
