Warianty tytułu
Model oceny ryzyka powodzi bazujący na metodzie dyfuzji informacji i wykorzystujący sieci neuronowe
Języki publikacji
Abstrakty
Climate change has caused more frequent floods in China which have already resulted in huge losses. Thus flood risk assessment and management is an important research topic. In this paper, a new model of flood risk assessment is proposed based on the information diffusion theory and the back propagation (BP) neural network. Due to the fact that flood statistics data are relatively short and often insufficient for flood risk assessment, the information diffusion method can transform imperfect flood historical data from a point in a traditional data sample to a fuzzy data set and obtain optimized data sample. Then, the optimized data are used to train neural networks with back propagation and can improve neural network adaptive ability. The flood data of Dongting Lake’s different encirclement dikes are used to assess the flood risk of Dongting Lake with the proposed model in this research. The results are consistent with the actual situation of Dongting Lake area, which thus verifies the model’s effectiveness for flood risk management. This method can be easily applied to effectively resolve problems of insufficient samples in flood risk assessment.
W artykule zaprezentowano nowy model oceny ryzyka powodzi bazujący na teorii dyfuzji informacji I wykorzystujący sieci neuronowe. Dane statystyczne o powodziach są relatywnie krótkie i często niewystarczające do oceny ryzyka. W pierwszym etapie przetwarza się dane historyczne do otrzymania bardziej kompletnych danych. Te dane wykorzystane są do trenowania sieci neuronowych.
Czasopismo
Rocznik
Tom
Strony
33-36
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, chenjunfei@yahoo.com.cn
- Business School, Hohai University
autor
- Business School, Hohai University, jinqiongji0431@yahoo.com.cn
autor
- Business School, Hohai University, hmwang@hhu.edu.cn
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University
autor
- Business School, Hohai University, shufangzhao@yahoo.com.cn
Bibliografia
- [1] Huang M.S., Huang C.C., Research on Grade Model of Flood Risk Assessment, Journal of Catastrophology, 22(2007), No. 1,1-5 (In Chinese)
- [2] Wang J. H., Risk Evaluation on Flood Disaster Based on the Fuzzy Comprehensive Assessment Method, Water Conservancy Science and Technology and Economy, 15(2009), No. 4,338-340 (In Chinese)
- [3] Jiang W.G., Li J., Chen Y.H., Risk Assessment System for Regional Flood Disaster: Principle and Method, Journal of Natural Disasters, 17(2008), No. 6, 53-59 (In Chinese)
- [4] Luo P., Zhang T.R., Du J., GIS and Fuzzy Evaluation Method Based on Hazard Evaluation for Flood Disaster: A Case Study of Chongqing,China, Journal of Xihua Normal-University, 28(2007),No. 2, 165 -171(2007). (In Chinese)
- [5] Wei Y.M., Xu W.X., Fan Y., Artificial Neural Network Based Predictive Method for Flood Disaster, Computers & Industrial Engineering, 42(2002), 383-390.
- [6] Huang Z.W., Zhou J.Z., Song L.X., Flood Disaster Loss Comprehensive Evaluation Model Based on Optimization Support Vector Machine, Expert Systems with Applications,37(2010), 3810-3814.
- [7] Zhou H.C., Zhang D., Assessment Model of Drought and Flood Disasters with Variable Fuzzy Set Theory, Transactions of the CSAE, 25 (2009), No. 9, 56-61 (In Chinese)
- [8] Chen Y. N., Yang S. Q., Application of Grey Clustering Analysis in the Classification of Flood Disaster Grade, Arid Land Geography , 22(1999), No. 3,37-41 (In Chinese)
- [9] Feng L. H., Luo G.Y., Flood risk analysis based on information diffusion theory, Human & Ecological Risk Assessment ,14(2008),No. 6,1330-1337
- [10] Chatman E. A., Diffusion Theory: a Review and Test of a Conceptual Models in Information Diffusion, Journal of the American Society for Information Science, 37(1986), No. 6, 377-386.
- [11] Huang C. F. , Principle of Information Diffusion, Fuzzy Sets and Systems, 91(1997),No.1, 69-90
- [12] He S., Wang J. D., Wang H., Han X. M., The Evaluation of Loess Slope Stability Based on Combination of Information Diffusion Theory and BP Neural Network, Journal of Northwest University, 38(2008), No. 6, 983-988 (In Chinese)
- [13] Huang C.F., Wang J.D., Fuzzy Information Optimization Technology and Its Application, University of Aeronautics & Astronautics Press, Beijing (1995). (In Chinese)
- [14] Kermanshahi B., Iwamiya H., Up to Year 2020 Load Forecasting Using Neural Nets. Electrical Power and Energy Systems, 24(2002), 789–797.
- [15] Enke D., Thawornwong, S., The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns, Expert Systems with Applications, 29(2005), No. 4, 927-940.
- [16] Azadeh A., Ghaderi S. F., Sohrabkhani S., Annual Electricity Consumption Forecasting by Neural Network in High Energy Consuming Industrial Sectors, Energy Conversion and Management, 49(2008), 2272-2278.
- [17] Mao D.H., Research on Theory and Method and Application of Synthetic Risk Analysis on Flood Disaster, China Water Conservancy and Hydropower Press,Beijing(2009). (In Chinese)
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-e97f470c-2216-4e4b-826e-64c8ff4b54ea