A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

Abstract

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

“…Reference [5] applied NFES to compute land sliding susceptibility using statistical index (WI). Reference [22] collected geographical information to pass through six different membership functions for measuring land sliding susceptibility using NFES. Many researchers have analysed combination of artificial neural network and fuzzy inference system [29][30][31][32].…”

“…The traceability between the data extracted and the conclusion drawn has been presented through bubble plots and frequency plots. The quality assessment problem is concerned with the quality of study selection [5,22,23]. The systematic mapping results were considered in regard to the seismic domain, and the validity of the conclusions drawn concerns the earthquake prediction context only.…”

Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services

“…The AHP method was proposed by Thomas L. Saaty (1980) [42]. This method is a widely used multiple criteria decision-making approach in GIS spatial analysis [43]. Although the AHP method aims to capture experts' knowledge, the conventional AHP still cannot take into account the human thinking style.…”

“…The traditional AHP method has some limitations by using the exact values to express the decision-makers' judgments in a comparison of alternatives [25,44]. As the comparison of indicators in the traditional AHP method is based on expert judgments, some degree of inconsistency may transfer from uncertain comparisons to the results [39][40][41][42][43]45].…”

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

“…As mentioned above, various studies have successfully used the ANFIS for landslide susceptibility assessment [33,34]. However, hybrid ensembles of this model have been broadly used for similar applications like flood [35] and forest fire susceptibility [36] assessment.…”