Artificial Intelligence-Based Image Classification Techniques for Hydrologic Applications

Abstract

Hydrologic modeling is a complex phenomenon dependent on numerous parameters. Since the estimation of parameters is subjected to high uncertainty due to high spatial variation. Therefore, the accuracy of each parameter becomes prime necessary for hydrologic modeling. Gene Expression Programming (GEP) is employed for the first time for Land Use Land Cover (LULC) classification. In the present study, five AI techniques, namely Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), the M5 Model tree, the multivariate adaptive regression splines (MARS), and Gene Expression Programming (GEP), were studied comparatively for their image classification capability. Comparison criteria adopted for considered AI techniques were errors estimators’ (omission and commission errors) and accuracy estimators’ (overall accuracy and Kappa coefficient). Based on the obtained results, the performance of the GEP technique is found very much comparable with SVM and ANFIS based on overall & Kappa coefficient (>0.85). GEP has a significant advantage over other techniques in producing mathematical functions for the given set of input and output parameters. The present study recommends the use of the GEP technique for LULC image classification.