Research on Regional Flood Disaster Risk Assessment Based on PCA and BP Neural Network

Jin, Qiongji; Chen, Junfei; Wang, Huimin; Zhao, Shufang

doi:10.1109/iceee.2010.5660290

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Additionally, the method is widely applicable and effective, and it provides a strong nonlinear mapping capacity. Thus, it is ideal for studies in the field of natural disasters [18,[60][61][62][63][64][65][66]. The overall accuracy of the snow disaster early warning model based on the BP-ANN method in this study reached 80%.…”

Section: Discussionmentioning

confidence: 79%

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

Gao

Huang

et al. 2017

Remote Sensing

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Abstract:It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 • C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot).

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Section: Discussionmentioning

confidence: 79%

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

Gao

Huang

et al. 2017

Remote Sensing

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“…A back propagation neural networks (BPNN) has been used in (Jin et al, 2010)to estimate the possibility of a flood disaster. To oversee optimal power generation in north-eastern Thailand, researchers in (Surussavadee & Wu, 2015)suggested a neural network-based wind forecast approach.…”

Section: Introductionmentioning

confidence: 99%

River flow modeling for flood prediction using machine learning techniques in Godavari river, India

Shukla,

Mishra,

Goel

2023

The Scientific Temper

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Floods are highly impactful natural calamities, inflicting significant damage to infrastructure and causing numerous fatalities. These devastating events occur when rivers exceed their capacity or breach their banks due to intense precipitation.Forecasting river flow in the minimum Godavari river basin of eastern India allowed researchers to examine the potential of four data-driven techniques, including the artificial neural network (ANN), support vector machine (SVM), nonlinear autoregressive network with exogenous input (NARX) and Gaussian process regression (GPR), and compare the outcomes to those of the proposed neuro-tree method. By combining values of the antecedent river flow from two gauging stations, various models were built utilizing the methodologies, and the results were compared to see which models had the best match. The performances of the generated models were examined using mean square error, coefficient of correlation (R), and Nash-Sutcliffe coefficient, three widely used statistical performance assessment metrics (NS).An extensive analysis of the overall performance indicators revealed that the proposed neuro-tree algorithm models were more effective in flood prediction than the other four techniques employed in this study.

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Specification of Operating Rules for Water Reservoir to Manage Flood Using Z-Notation

Alam

2021

VAWKUM trans. comput. sci.

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Flood is natural event; it brings a lot of destruction. Due to flood not only loss of human life and misery of millions of people occur, but tremendous damages occur in public and private property. It cannot be stopped, but can be managed to save the lives and property of the people. To manage a flood, there are two solutions, physical and logical. In physical solutions, different physical constructions are made to manage flood like levee, channel improvement, flood ways, and widening of barrages, but these control works have many deficiencies. In logical solutions, models and techniques are developed to manage the flood, however, these systems also have some deficiencies. To overcome the deficiencies in the existing models, we focus on to develop a flood management model consisting of an off-river reservoir and diverted canals having regulators. Reservoirs are most effective water storage that smooth down extreme inflow. The optimal operations of reservoirs determine the release and accumulation of water over time. For effective operative decision, operating rules will be defined in Z-Specification. For these operating rules, we will develop an algorithm, will verify the rules in Z-Notation and will implement them in the Java programming language.

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Research on Regional Flood Disaster Risk Assessment Based on PCA and BP Neural Network

Cited by 3 publications

References 2 publications

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

River flow modeling for flood prediction using machine learning techniques in Godavari river, India

Specification of Operating Rules for Water Reservoir to Manage Flood Using Z-Notation

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