Groundwater potential assessment mapping from GaoFen-6 satellite data by a deep network model

Abstract

Groundwater is an important fresh water resource, which is very important for human survival. We analyzed groundwater potential assessment (GPA) by using back propagation (BP) neural network model and analytic hierarchy process (AHP) model in remote sensing and geographic information system in the north of Zhuhai City, Guangdong Province, China. We have used a variety of factors related to groundwater. These models were based on the relationship between groundwater supply potential and hydrogeological factors. GaoFen-6 remote sensing image was first collected, processed and got lithology, relief, slope, flow accumulation (FA), land temperature, soil humidity and vegetation coverage. Water yield data were collected from 36 well locations. We determined the weight of each index by using BP neural network and AHP. The results were fitted with the actual well water yield. The R2 of BP neural network and AHP models for GPA were 0.89125 and 0.85946, respectively. The results show that BP neural network model can eliminate the influence of subjective factors on the results. It is BP neural network model that more suitable for the development and utilization of groundwater resources than AHP model.