Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images

The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development.

Automated summary

Quantitative spatial analysis using GIS and R software has been an effective tool to study natural hazards and their interactions. This study developed multi-hazard susceptibility maps using data mining techniques, GIS tools, and unmanned aerial vehicles. By applying linear regression models and seven classifiers, the study identified the most influential morphometric parameters on collapsed pipes, gully heads, and landslides. The results showed that the majority of the study region had low susceptibility to these hazards. The validation results indicated high accuracy of the applied models. The study highlighted the importance of understanding the interrelated effects of multiple hazards for sustainable environmental management and socio-economic development.