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.

Flood is one of the most dangerous environmental hazards that threaten the human lives and properties on a large scale. Identification of flood risk areas with the aim of optimal management of this area is very necessary. In this research, the estimation of Land use/Land cover (LULC) in the Marand basin was modelled on the Sub-basin level using remote sensing and geographic information systems (GIS). The runoff coefficient was determined using the LULC extracted from satellite images, slope map and soil hydrologic groups, and rainfall intensity. Then, peak runoff for each sub-basin was calculated. In the following, by using linear membership function in the fuzzy logic model, the integration of two layers of peak runoffs and the elevation line layers between 0 and 1 were transformed into fuzzy values. Afterward, by applying multiple overlaps of weights to each of these two layers and their results, classes of flood hazard distribution map were produced. By comparing the hazard map with the results of participatory geographic information system (PGIS) and entering this information into the confusing matrix, the collision accuracy of the map was 87.83%. Finally, by comparing this map with the land cover/use map, their flood extent was determined separately. ARTICLE HISTORY

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