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Early warning and detection of geological disasters based on intelligent genetic algorithm

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Del Ventisette, C., Garfagnoli, F., Ciampalini, A., Battistini, A., Gigli, G., Moretti, S., & Casagli, N. (2012). An integrated approach to the study of catastrophic debris-flows: geological hazard and human influence. Natural Hazards and Earth System Sciences, 12(9), 2907-2922. Search in Google Scholar

TU, S., ZHANG, Z., FU, H., XU, S., DENG, M., HE, L., & LIU, J. (2022). Geological hazard susceptibility evaluation based on CF and CF-LR model. The Chinese Journal of Geological Hazard and Control, 33(2), 96-104. Search in Google Scholar

Lixin, Y., Lingling, G., Dong, Z., Junxue, Z., & Zhanwu, G. (2012). An analysis on disasters management system in China. Natural hazards, 60, 295-309. Search in Google Scholar

Shi, P., Xu, W., & Wang, J. A. (2016). Natural disaster system in China (pp. 1-36). Springer Berlin Heidelberg. Search in Google Scholar

Yao, Z. (2020, May). Characteristics, challenges and suggestions of geological disaster prevention and control in China. In IOP conference series: Earth and environmental science (Vol. 514, No. 2, p. 022025). IOP Publishing. Search in Google Scholar

Hongdong, L. U. O., Ruidong, L. I., Bo, Z. H. A. N. G., & Bo, C. (2019). An early warning model system for predicting meteorological risk associated with geological disasters in the Longnan area, Gansu Province based on the information value method. Earth Science Frontiers, 26(6), 289. Search in Google Scholar

Ouyang, C., Wang, Z., An, H., Liu, X., & Wang, D. (2019). An example of a hazard and risk assessment for debris flows—A case study of Niwan Gully, Wudu, China. Engineering Geology, 263, 105351. Search in Google Scholar

Lyu, H. M., Shen, S. L., Yang, J., & Zhou, A. N. (2020). Risk assessment of earthquake-triggered geohazards surrounding Wenchuan, China. Natural Hazards Review, 21(3), 05020007. Search in Google Scholar

Park, J. Y., Lee, S. R., Lee, D. H., Kim, Y. T., & Lee, J. S. (2019). A regional-scale landslide early warning methodology applying statistical and physically based approaches in sequence. Engineering Geology, 260, 105193. Search in Google Scholar

Qiu, L., Liu, Z., Wang, E., He, X., Feng, J., & Li, B. (2020). Early-warning of rock burst in coal mine by low-frequency electromagnetic radiation. Engineering Geology, 279, 105755. Search in Google Scholar

Toulkeridis, T., Porras, L., Tierra, A., Toulkeridis-Estrella, K., Cisneros, D., Luna, M., ... & Salazar, R. (2019). Two independent real-time precursors of the 7.8 Mw earthquake in Ecuador based on radioactive and geodetic processes—Powerful tools for an early warning system. Journal of Geodynamics, 126, 12-22. Search in Google Scholar

Mei, G., Xu, N., Qin, J., Wang, B., & Qi, P. (2019). A survey of Internet of Things (IoT) for geohazard prevention: Applications, technologies, and challenges. IEEE Internet of Things Journal, 7(5), 4371-4386. Search in Google Scholar

Wang, X., Wang, C., & Zhang, C. (2020). Early warning of debris flow using optimized self-organizing feature mapping network. Water Supply, 20(7), 2455-2470. Search in Google Scholar

Chen, C. Y. (2020). Event-based rainfall warning regression model for landslide and debris flow issuing. Environmental Earth Sciences, 79(6), 127. Search in Google Scholar

Wang, C. L., & Li, S. W. (2018). Hybrid fruit fly optimization algorithm for solving multi-compartment vehicle routing problem in intelligent logistics. Advances in Production Engineering & Management, 13(4), 466. Search in Google Scholar

Fernandes, F., Sousa, T., et al. (2011). Genetic algorithm methodology applied to intelligent house control, 2011 IEEE Symposium on Computational Intelligence Applications In Smart Grid (CIASG), IEEE, 1-8. Search in Google Scholar

Ali, W., & Ahmed, A. A. (2019). Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm‐based feature selection and weighting. IET Information Security, 13(6), 659-669. Search in Google Scholar

Ko, M. D. (2021). An intelligent, empty container dispatching system model using fuzzy set theory and genetic algorithm in the context of industry 4.0. Enterprise Information Systems, 15(9), 1298-1321. Search in Google Scholar

Mirjalili, S., & Mirjalili, S. (2019). Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, 43-55. Search in Google Scholar

Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4, 65-85. Search in Google Scholar

Goldberg, D. E., & Samtani, M. P. (1986, February). Engineering optimization via genetic algorithm. In Electronic computation (pp. 471-482). ASCE. Search in Google Scholar

Sobey, A., Blanchard, J., Grudniewski, P., & Savasta, T. D. (2019). There’s no free lunch: a study of Genetic Algorithm use in Maritime Applications. Search in Google Scholar

Zainuddin, F. A., Abd Samad, M. F., & Tunggal, D. (2020). A review of crossover methods and problem representation of genetic algorithm in recent engineering applications. International Journal of Advanced Science and Technology, 29(6s), 759-769. Search in Google Scholar

Barthwal, M., Dhar, A., & Powar, S. (2021). The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications. Applied Energy, 283, 116253. Search in Google Scholar

Mustafi, D., & Sahoo, G. (2019). A hybrid approach using genetic algorithm and the differential evolution heuristic for enhanced initialization of the k-means algorithm with applications in text clustering. Soft Computing, 23, 6361-6378. Search in Google Scholar

Zhao, H., Liu, K., Li, S., Yang, F., Cheng, S., Eldeeb, H. H., ... & Xu, G. (2021). Shielding optimization of IPT system based on genetic algorithm for efficiency promotion in EV wireless charging applications. IEEE Transactions on Industry Applications, 58(1), 1190-1200. Search in Google Scholar

Fang, C., Tao, Y., Wang, J., You, H., Cui, Y., & Zhou, M. (2021). Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Porcelain Insulator. Frontiers in Energy Research, 779. Search in Google Scholar

Pisner, D. A., & Schnyer, D. M. (2020). Support vector machine. In Machine learning (pp. 101-121). Academic Press. Search in Google Scholar

Yao, L., Fang, Z., Xiao, Y., Hou, J., & Fu, Z. (2021). An intelligent fault diagnosis method for lithium battery systems based on grid search support vector machine. Energy, 214, 118866. Search in Google Scholar

Robles-Velasco, A., Cortés, P., Muñuzuri, J., & Onieva, L. (2020). Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliability Engineering & System Safety, 196, 106754. Search in Google Scholar

Zhang, F., & O'Donnell, L. J. (2020). Support vector regression. In Machine learning (pp. 123-140). Academic Press. Search in Google Scholar

Fang, C., Tao, Y., Wang, J., Ding, C., Huang, L., Zhou, M., ... & Wang, Y. (2021). Mapping relation of leakage currents of polluted insulators and discharge arc area. Frontiers in Energy Research, 9, 777230. Search in Google Scholar

Roy, A., Manna, R., & Chakraborty, S. (2019). Support vector regression based metamodeling for structural reliability analysis. Probabilistic Engineering Mechanics, 55, 78-89. Search in Google Scholar

Moustapha, M., Bourinet, J. M., Guillaume, B., & Sudret, B. (2018). Comparative study of Kriging and support vector regression for structural engineering applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(2), 04018005. Search in Google Scholar

Ghosh, S., Roy, A., & Chakraborty, S. (2018). Support vector regression based metamodeling for seismic reliability analysis of structures. Applied Mathematical Modelling, 64, 584-602. Search in Google Scholar

eISSN:
2444-8656
Lingua:
Inglese
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Volume Open
Argomenti della rivista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics