Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization

This paper investigates three techniques for spatial mapping and the consequential hydrologic inversion, using hydraulic conductivity (or transmissivity) and hydraulic head as the geophysical parameters of concern. The data for the study were obtained from the Waste Isolation and Pilot Plant (WIPP) site and surrounding area in the remote Chihuahuan Desert of southeastern New Mexico. The central technique was the Radial Basis Function algorithm for an Artificial Neural Network (RBF-ANN). An appraisal of its performance in light of classical and temporal geostatistical techniques is presented. Our classical geostatistical technique of concern was Ordinary Kriging (OK), while the method of Bayesian Maximum Entropy (BME) constituted an advanced, spatio-temporal mapping technique. A fusion technique for soft or inter-dependent data was developed in this study for use with the neural network. It was observed that the RBF-ANN is capable of hydrologic inversion for transmissivity estimation with features remaining essentially similar to that obtained from kriging. The BME technique, on the other hand, was found to reveal an ability to map localized lows and highs that were otherwise not as apparent in OK or RBF-ANN techniques.