Flood Disaster Evaluation Model Based on Kernel Dual Optimization Support Vector Machine

Deng, Weiping; Zhou, Jianzhong; Qiang, Zhimin; Xiao, Jian; Zhang, Yongchuan; Hua, Weihua

doi:10.3923/itj.2013.2412.2418

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The recent improvements in the efficiency of remote sensing (RS) and geographic information system (GIS) technologies have initiated a revolution in hydrology, particularly in flood management, which can fulfil all the requirements for flood prediction, preparation, prevention, and damage assessment (Tehrany, Pradhan, & Jebur 2013). Among different GIS-based flood models presented in the literature, artificial neural networks (Kia et al, 2011), frequency ratio (FR) , logistic regression (Pradhan 2010), adaptive network-based fuzzy inference system (Chau et al 2005), multi-layered feed forward network (Kar et al, 2015), decision trees (Tingsanchali & Karim 2010;Merz et al 2013;Tehrany et al 2013), and support vector machines (SVMs) (Zhou et al 2013;Tehrany et al 2014) are the most widespread techniques that utilize RS and GIS tools. Although flood forecasting and prediction models are available, the accuracy of flood prediction maps remains a critical issue.…”

Section: Introductionmentioning

confidence: 99%

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi

Pradhan

Nampak

et al. 2017

Geomatics, Natural Hazards and Risk

343

132

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In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.

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Section: Introductionmentioning

confidence: 99%

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi

Pradhan

Nampak

et al. 2017

Geomatics, Natural Hazards and Risk

343

132

View full text Add to dashboard Cite

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Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Allegri,

Zanetti,

Torresan

et al. 2024

Science of The Total Environment

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Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

2020

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Safe Routes to School is very important for students to have good physical and psychologically healthy in school life. For providing safe routes based on risk analysis, finding out dangerous points and areas can be a target to avoid dangerous locations by pedestrians and drivers. However, analyzing the risk assessment to derive the safe routes requires a large amount of data with a certain time of observation by experts. Deep learning is a solution to provide information regarding safe routes based on expert knowledge. In this paper, we propose a risk assessment inference approach using a Recurrent Neural Network (RNN) model with Long-Short Term Memory (LSTM) cells based on geographical information for safe routes to school. However, geographical information including coordinates is difficult used in learning-based inference models because of the series of float values. For training the RNN model with the geographical data, coordinates of routes and danger points are translated to be geohash through the geohash converter. The geohash data with other data of features are fused and inputted to the one-hot encoder. The one-hot encoded data is used in the inputs of the RNN model to train the LSTMs. The input data of the training model is derived by the risk index model that is proposed to calculate the risk index based on distances of route coordinates and danger points. Therefore, the risk index is correlated with the training dataset. Through the proposed inference approach, the geographical information including multiple coordinates is enabled to be trained by RNN as a geohash-based input string. Moreover, the input string with other features is fused to support the one-hot encoding to get a better result in RNN models.

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Flood Disaster Evaluation Model Based on Kernel Dual Optimization Support Vector Machine

Cited by 3 publications

References 6 publications

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Risk Assessment Inference Approach Based on Geographical Danger Points Using Student Survey Data for Safe Routes to School

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