Ecological-environmental quality estimation using remote sensing and combined artificial intelligence techniques

Abstract

Ecological-environmental quality was evaluated for Tabriz and Rasht cities (in Iran) with different climate conditions using artificial intelligence (AI) and remote sensing (RS) techniques. Sampling sites were surveyed and ecological experts assigned eco-environment background values (EBVs) of sites. Then, eco-environmental attributes were extracted as RS derived, and meteorological attributes were observed. Three AI-based models, artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were then applied to learn the relationship between a target set of known EBVs and eco-environmental attributes as inputs. According to the results of the single models, none of the models could evaluate EBV appropriately for all regions and classes. Thereafter, three combining techniques were applied to the outputs of single models to enhance spatial evaluation of EBV. It was observed that the modeling for Tabriz led to more accurate results. It seems that the better network performance for Tabriz may be due to a more heterogeneous dataset in this kind of climate. Furthermore, results indicated that SVR led to better performance than both ANN and ANFIS models, but the models’ combining techniques were shown to be superior. Combining techniques enhanced performance of single AI modeling up to 26% in the verification step.