SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment

Abstract

Since it is not possible to determine the exact time of a natural disaster’s occurrence and the amount of physical and financial damage on humans or the environment resulting from their event, decision-makers need to identify areas with potential vulnerability in order to reduce future losses. In this paper, a GIS-based open source software entitled Seismic-Related Vulnerability Calculation Software (SEVUCAS), based on the Step-wise Weight Assessment Ratio Analysis (SWARA) method and geographic information system, has been developed to assess seismic vulnerability by considering four groups of criteria (i.e., geotechnical, structural, socio-economic, and physical distance to needed facilities and away from dangerous facilities). The software was developed in C# language using ArcGIS Engine functions, which provide enhanced visualization as well as user-friendly and automatic software for the seismic vulnerability assessment of buildings. Weighting of the criteria (indicators) and alternatives (sub-indicators) was done using SWARA. Also, two interpolation methods based on a radial basis function (RBF) and teaching–learning-based optimization (TLBO) were used to optimize the weights of the criteria and the classes of each alternative as well. After weighing the criteria and alternatives, the weighted overlay analysis was used to determine the final vulnerability map in the form of contours and statistical data. The difference between this software and similar ones is that people with a low level of knowledge in the area of earthquake crisis management can use it to determine and estimate the seismic vulnerabilities of their houses. This visualized operational forecasting software provides an applicable tool for both government and people to make quick and correct decisions to determine higher priority structures for seismic retrofitting implementation.

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models

“…Scientists have used a variety of computational data mining methods and models in natural hazard research, including studies of floods [18][19][20][21][22][23][24][25][26][27][28], wildfire [29], sinkholes [30], droughtiness [31,32], earthquakes [33,34], land/ground subsidence [35,36], groundwater [21,[37][38][39][40][41][42][43][44], and landslides [22,. These methods extract related patterns in historical data to predict future events [73].…”

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

“…For example, Van Dao et al [3] reported that deep learning ANN requires much more computational time than conventional methods. Despite this disadvantage, machine learning methods have the ability to handle large volumes of non-linear and complex data derived from different sources and reported at a variety of scales in many fields especially in studies of natural hazards such as floods [18][19][20][21][22][23][24][25][26][27][28], wildfires [29,30], sinkholes [31], drought [32,33], earthquakes [34,35], gully erosion [36,37], and land/ground subsidence [38,39].…”

Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

“…Recent developments of machine learning (ML) have introduced new optimization algorithms, which could be used for optimizing weights for membership function of the neural fuzzy model. Furthermore, ML techniques have recently gained a good attention among environmental modeling research community as they are advantageous in efficiently capturing the complex relationship between the environmental predictors and the response, such as flood [55][56][57][58][59][60][61][62][63], earthquake [64,65], wildfire [66], sinkhole [67], droughtiness [68], gully erosion [69,70], groundwater [71][72][73][74] and land/ground subsidence [75], and landslide in this case [54,59,. Nevertheless, investigation of new optimization algorithms and the neural fuzzy has not been carried out.…”