2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 1 , PP: 293-304, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Domi Evangeline . S 1 * , G. Usha 2
Doi: https://doi.org/10.54216/JISIoT.140123
Landslides establish a main geologic threat of strong concern in many parts of the world. The vigor of soil, rocks, or other rubbish moving down a slope can destroy whatever in its track. Landslides happen in an extensive variety of geological and structural settings, geomechanical contexts, and as a response to numerous triggering and loading procedures. They are frequently related to other main natural disasters like floods, earthquakes, and volcanic waves. Landslides occasionally attack without noticeable warning. While only some cases have been examined the earlier, modern monitoring models are certain to deliver a wealth of novel quantitative observations based on SAR (synthetic aperture radar) and GPS technology for mapping the surface velocity area. This study emphasizes the latent of incorporating advanced machine learning (ML) models with geophysical data to improve prediction of landslides and risk management strategies. This study develops a Predicting Landslides with frictional-based Deep Learning using Spider Wasp Optimizer (PLFFDL-SWO) Method. The major intention of the PLFFDL-SWO technique lies in the robust frictional force based on predicting landslides. In the presented PLFFDL-SWO model, Z-score normalization is performed to transform the raw data into compatible format. Then, the long short-term memory (LSTM) model is utilized for the prediction of landslides. LSTM is a recurrent neural network (RNN) type, for predicting landslides based on frictional force data. Traditional landslide prediction methods often struggle with temporal dynamics and nonlinear relationships inherent in geophysical data. Finally, the spider wasp optimizer (SWO) algorithm is exploited for the optimal hyper parameter adjustment of the LSTM model to improve prediction accuracy. The experimentation result investigation of the PLFFDL-SWO technique can be examined by employing a benchmark dataset. The simulation outcomes reported the supremacy of the PLFFDL-SWO technique under different measures
Landslide Prediction , Deep Learning , Spider Wasp Optimizer , Long Short-Term Memory , Frictional Force
[1] Peng L et al (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China. Geomorphology 204:287–301
[2] Gabet, E.A., 2007. Theoretical model coupling chemical weathering and physical erosion in landslide-dominated landscapes. Earth. Planet. Sci. Lett. 264, 259–265.
[3] Wang Y et al (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993
[4] Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., Reichenbach, P., 2008. Comparing landslide inventory maps. Geomorphology 94, 268–289.
[5] Erener, A., Duzgun, H.S.B., 2010. Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway). Landslides 7, 55–68
[6] Wang Y et al (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445
[7] Gariano, S.L., Sarkar, R., Dikshit, A., Dorji, K., Brunetti, M.T., Peruccacci, S., Melillo, M., 2019. Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan. Bull. Eng. Geol. Environ. 78, 4325–4332
[8] Pham BT et al (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63.
[9] Shou K-J, Lin J-F (2016) Multi-scale landslide susceptibility analysis along a mountain highway in Central Taiwan. Eng Geol 212:120–135.
[10] Al-Najjar, H.A.H., Pradhan, B., Kalantar, B., Sameen, M.I., Santosh, M., Alamri, A., 2021. Landslide susceptibility modeling: an integrated novel method based on machine learning feature transformation. Remote Sens. 13, 3281.
[11] Meng, S., Shi, Z., Li, G., Peng, M., Liu, L., Zheng, H. and Zhou, C., 2024. A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm. Computers and Geotechnics, 167, p.106106.
[12] Alqadhi, S., Mallick, J. and Alkahtani, M., 2024. Integrated deep learning with explainable artificial intelligence for enhanced landslide management. Natural Hazards, 120(2), pp.1343-1365.
[13] Xu, Y., Ouyang, C., Xu, Q., Wang, D., Zhao, B. and Luo, Y., 2024. CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. Scientific Data, 11(1), p.12.
[14] Guerrero-Rodriguez, B., Garcia-Rodriguez, J., Salvador, J., Mejia-Escobar, C., Cadena, S., Cepeda, J., Benavent-Lledo, M. and Mulero-Perez, D., 2024. Improving landslide prediction by computer vision and deep learning. Integrated Computer-Aided Engineering, 31(1), pp.77-94.
[15] Chen, T., Gao, X., Liu, G., Wang, C., Zhao, Z., Dou, J., Niu, R. and Plaza, A., 2024. BisDeNet: A new lightweight deep learning-based framework for efficient landslide detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[16] Ma, Z. and Mei, G., 2024. Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations. Journal of Rock Mechanics and Geotechnical Engineering.
[17] Yang, C., Yin, Y., Zhang, J., Ding, P. and Liu, J., 2024. A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning. Geoscience Frontiers, 15(1), p.101690.
[18] Al-Faiz, M.Z., Ibrahim, A.A. and Hadi, S.M., 2018. The effect of Z-Score standardization (normalization) on binary input due the speed of learning in back-propagation neural network. Iraqi Journal of Information and Communication Technology, 1(3), pp.42-48.
[19] Zitti, M., 2024. Forecasting salmon market volatility using long short-term memory (LSTM). Aquaculture Economics & Management, 28(1), pp.143-175.
[20] Osama, S., Ali, A.A. and Shaban, H., 2024. Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm. Neural Computing and Applications, pp.1-18.
[21] https://www.kaggle.com/datasets/adizafar/landslide-prediction-for-muzaffarabadpakistan
[22] Aslam, B., Zafar, A. and Khalil, U., 2021. Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential. Soft Computing, 25(21), pp.13493-13512.
[23] Saha, S., Bera, B., Shit, P.K., Sengupta, D., Bhattacharjee, S., Sengupta, N., Majumdar, P. and Adhikary, P.P., 2023. Modelling and predicting of landslide in Western Arunachal Himalaya, India. Geosystems and Geoenvironment, 2(2), p.100158.
