Abstract
Flood disasters cause serious economic losses to our country every year, which affects the rapid development and construction of our country’s economy to a certain extent. Our country’s mountain floods mostly occur in mountainous hills and other rural areas with relatively backward economic development. Disaster relief and reconstruction ability in these places are relatively weak. To provide an important basis for making decisions on flood prevention and mitigation, we must first be able to scientifically, accurately and efficiently assess the losses caused by flood disasters. Aiming at the problems of low assessment accuracy, heavy workload and cumbersome operation in the disaster assessment work of Dangtian in the disaster-affected area, this paper starts from the analysis of the disaster mechanism and attribute characteristics of mountain floods and proposes an RBF neural network integration based on rough set flood damage loss assessment model of, and the flood loss assessment model based on neural network integration was applied in the flood loss prediction work of Village A, making the flood loss rate suitable for the use of the flood loss assessment model. The forecast results are compared and analyzed with the actual statistically published flood loss value, which verifies the feasibility of the forecast model and provides a new method for flood loss assessment. The prediction results of mountain flood disaster loss in villages and towns show that the research based on the improved RBF neural network earthquake damage loss prediction can quickly and accurately obtain more reliable evaluation results in terms of direct economic loss and casualty assessment, which improves rescue efficiency and reduces the affected population. It has significant practicality in disaster reduction and rescue operations, industrial layout planning, and economic benefit evaluation.
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References
Ying W, Xiao L, Xuesong Q et al (2016) Prediction-based survivable virtual network mapping against disaster failures. Networks 26(5):336–354
Sasaki D, Moriyama K, Ono Y (2018). Hidden common factors in disaster loss statistics: a case study analyzing the data of Nepal (special issue on the development of disaster statistics). J Disaster Res. 13(6): 1032–1038
Murray D, Joe C, Heather H (2016) Urban flood prediction and warning–challenges and solutions. Proc Water Environ Federation 2016(13):2237–2242
Li G (2019) An integrated model of rough set and radial basis function neural network for early warning of enterprise human resource crisis. Int J Fuzzy Syst 21(8):2462–2471
Jiang RC, Jiang XJ, Gu SX (2017) Current status and counter-measures for existing problems in mountain torrent disaster prevention and control in Yunnan Province. J Information Optimization Sci 38(7):1169–1179
Zhou JQ, Pang ZL, Cai QG et al (2017) Susceptibility zoning of different types of mountain torrent disasters in the Yangtze River Basin of sourthern China. Bjing Linye Daxue Xuebao/J Bjing For Univ 39(11):56–64
Zhang XL, Xue YG, Qiu DH et al (2019) Multi-index classification model for loess deposits based on rough set and bp neural network. Pol J Environ Stud 28(2):953–963
Jianrong F, Xiyu Z, Fenghuan S et al (2017) Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data. J Mt Sci 14:1677
Selvi S, Chandrasekaran M (2020) Framework to forecast environment changes by optimized predictive modelling based on rough set and Elman neural network. Soft Comput 24(14):10467–10480
Qiang C, Zhuo Q, Zhang G, Zhao Y, Sun B, Gu P (2016) Robust modelling and prediction of thermally induced positional error based on grey rough set theory and neural networks. Int J Adv Manuf Technol 83(5–8):753–764
Cai J (2017) Construction license mechanism of mountain tunnels based on inrush prediction of fracture zones. Yanshilixue Yu Gongcheng Xuebao/Chinese J Rock Mech Eng 36(4):964–976
Zhu J, Cao Z, Zhang T et al (2018) Sufficient Condition for the Existence of the Compact Set in the RBF Neural Network Control. IEEE Transactions Neural Netw Learn Syst 29(7):3277–3282
Clarke GKC (2017) Glacier outburst floods from “Hazard Lake”, Yukon territory, and the problem of flood magnitude prediction. J Glaciol 28(98):3–21
Adib A, Mahmoodi A (2016) Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. Ksce J Civil Eng 21(1):1–11
Fei X, Youfu S, Xuejun R (2018) A rough set data prediction method based on neural network evaluation and least squares fusion. Clust Comput 22(1):1–6
Thorndahl S, Nielsen JE, Jensen DG (2016) Urban pluvial flood prediction. Water Sci Technol 74(11):2599–2610
Lei Y, Jianzhong Z, Gupta HV et al (2016) Efficient estimation of flood forecast prediction intervals via single- and multi-objective versions of the LUBE method. Hydrol Process 30(15):2703–2716
Upadhyay A, Anthal J, Shukla S (2019) Enhanced classification of LESS-III satellite image using rough set theory and ANN. Int J Cloud Comput 8(3):249–257
Adib A, Mahmoodi A (2017) Prediction of suspended sediment load using ANN GA conjunction model with Markov chain approach at flood conditions. KSCE J Civil Eng 21(1):447–457
Lin S-J (2017) Hybrid kernelized fuzzy clustering and multiple attributes decision analysis for corporate risk management. Int J Fuzzy Syst 19(3):659–670
Faralli S, Rittinghaus S, Samsami N, Distante D, Rocha E (2021) Emotional intensity-based success prediction model for crowdfunded campaigns. Inf Process Manag 58(1):102394
Anitha A, Acharjya DP (2018) Crop suitability prediction in Vellore district using rough set on fuzzy approximation space and neural network. Neural Comput Appl 30(12):3633–3650
Pushparaj, Sathyashrisharmilha, Arumugam, et al. (2018). Using 3D convolutional neural network in surveillance videos for recognizing human actions. Int Arab J Information Technol. 15(4):693–700
Huang MJ, Nie H, Ye C et al (2016) Comprehensive evaluation model for academic quality of food journals based on rough set and neural network. Adv J Food Sci Technol 11(1):64–70
Carcenac M, Soydan R et al (2016) A highly scalable modular bottleneck neural network for image dimensionality reduction and image transformation. Appl Intell: Int J Artif Intell, Neural Netw Complex Problem-Solving Technol 44(3):557–610
Acknowledgement
This work was supported by Science and Technology Program of Guangdong Province (2020B121201013); Science and Technology Special Fund Program of Guangdong Province (2020A0102009); Rural Science and Technology Commissioner Program of Guangdong Province (KTP20200278); National Natural Science Foundation of Guangdong Province (2021A1515012597); Collaborative Innovation Center of Big Data Research and Application, JYU & GMIP (130B0310); Research Achievement Award Cultivation Project, Jiaying University.
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Zhang, Y., Hao, Y. Loss prediction of mountain flood disaster in villages and towns based on rough set RBF neural network. Neural Comput & Applic 34, 2513–2524 (2022). https://doi.org/10.1007/s00521-021-05902-1
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DOI: https://doi.org/10.1007/s00521-021-05902-1