Flood hazard risk assessment model based on random forest

. This paper provides a review of multi-criteria decision-making  (MCDM) applications to flood risk management, seeking to highlight trends and identify research gaps. A total of 128 peer-reviewed papers published from 1995 to June 2015 were systematically analysed. Results showed that the number of flood MCDM publications has exponentially grown during this period, with over 82 % of all papers published since 2009. A wide range of applications were identified, with most papers focusing on ranking alternatives for flood mitigation, followed by risk, hazard, and vulnerability assessment. The analytical hierarchy process (AHP) was the most popular method, followed by Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW). Although there is greater interest in MCDM, uncertainty analysis remains an issue and was seldom applied in flood-related studies. In addition, participation of multiple stakeholders has been generally fragmented, focusing on particular stages of the decision-making process, especially on the definition of criteria weights. Therefore, addressing the uncertainties around stakeholders' judgments and endorsing an active participation in all steps of the decision-making process should be explored in future applications. This could help to increase the quality of decisions and the implementation of chosen measures.

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Multi-criteria decision-making for flood risk management: a survey of the current state of the art

Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China

Flood is one of the most common natural hazard that take place almost everywhere around the world except the polar regions. Flood damage can be reduced through implementing proper management and policies. Flood is a common hazard in Vaitarna basin, Maharashtra, and occurs once in every 2 to 3 years; it causes severe damage to life and properties. Present days, remote sensing and geographic information system are very comprehensive tools for the assessment of hydrological analysis and hazard management. These tools are also capable to deliver quite an accurate result in a cost- and time-effective manner. Thus, in this paper, an attempt has been made to identify critical zones, which are having a higher vulnerability to the flood in the Vaitarna basin through remote sensing and geospatial approach. Total nine influencing factors, such as elevation, slope, distance from the river, rainfall, flow accumulation, land use, geology, topographic wetness index, and curvature have been assessed individually as well as integrated in GIS software by assigning relative weights through the analytical hierarchy process (AHP) approach. As a result of this study, a final flood map has been prepared and the regions having very high flood potentiality are identified. The resultant map has shown about 20% of the total area in Vaitarna basin is having a very high probability of flood, and these regions are requiring some serious attention of governmental or non-governmental bodies to reduce the flood risk. The analytical hierarchy process (AHP) method proposed in this study is capable to provide an accurate result for flood mapping and can be easily applied to other regions around the world for the management and prevention of the flood hazard.

Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India

Flood is the most devastating and prevalent disaster among all-natural disasters. Every year, flood claims hundreds of human lives and causes damage to the worldwide economy and environment. Consequently, the identification of flood-vulnerable areas is important for comprehensive flood risk management. The main objective of this study is to delineate flood-prone areas in the Panjkora River Basin (PRB), eastern Hindu Kush, Pakistan. An initial extensive field survey and interpretation of Landsat-7 and Google Earth images identified 154 flood locations that were inundated in 2010 floods. Of the total, 70% of flood locations were randomly used for building a model and 30% were used for validation of the model. Eight flood parameters including slope, elevation, land use, Normalized Difference Vegetation Index (NDVI), topographic wetness index (TWI), drainage density, and rainfall were used to map the flood-prone areas in the study region. The relative frequency ratio was used to determine the correlation between each class of flood parameter and flood occurrences. All of the factors were resampled into a pixel size of 30×30 m and were reclassified through the natural break method. Finally, a final hazard map was prepared and reclassified into five classes, i.e., very low, low, moderate, high, very high susceptibility. The results of the model were found reliable with area under curve values for success and prediction rate of 82.04% and 84.74%, respectively. The findings of this study can play a key role in flood hazard management in the target region; they can be used by the local disaster management authority, researchers, planners, local government, and line agencies dealing with flood risk management.

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GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan.

This study presents the methodology and procedure for risk assessment of flood disasters in central Liaoning Province, which was supported by geographical information systems (GIS) and technology of natural disaster risk assessment. On the basis of the standard formulation of natural disaster risk and flood disaster risk index, of which weights were developed using combined weights of entropy, the relative membership degree functions of variable fuzzy set (VFS) theory were calculated using improved set pair analysis, while level values were calculated using VFSs, including hazard levels, exposure levels, vulnerability levels and restorability levels, and the flood risk level for each assessment unit was obtained using the natural disaster index method. Consequently, integrated flood risk map was carried out by GIS spatial analysis technique. The results show that the southwestern and central parts of the study area possess higher risk, while the northwestern and southeastern parts possess lower risk. The results got by the assessment model fits the area of historical flood data; this study offer new insights and possibility to carry out an efficient way for flood disaster prevention and mitigation. The study also provides scientific reference in flood risk management for local and national governmental agencies.

Xuehui Ren

Papers

Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China