Predicting groundwater potential assessment in water-deficient islands based on convolutional neural networks

Abstract

Due to the special hydrogeological features of the sea islands, the distribution of groundwater is difficult to assess. In order to improve the accuracy of the predicting groundwater potential assessment(GPA) in islands region. We tried to use the convolutional neural networks(CNN) and took lithology, aspect, slope, water density, vegetation fraction, soil humidity and land surface temperature as the remote sensing assessment indicators. The island area of Pearl River Estuary, China was selected as the study area. The groundwater situation of Wailingding Island was taken as the sample data of the total area, and the map of this island was divided into 24 × 22 grids. The levels of GPA of each small grid was determined by hydrogeological maps with results of geophysical method and wells or springs data. Meanwhile, the corresponding data were enlarged and the sample label data set was made. Using the CNN model of learning, training and testing, analysis of groundwater potential and each coupling correlation between remote sensing indicators constantly. After 1500 times of training, loss of model dropped to 0.3113, the accuracy of model was 96.96%. A good 5 levels classification prediction of GPA model was received. The AUC of ROC curve and significance level (P) of the CNN model were 0.855 and 0.001 respectively, which were better than the results of the classic GRSFAI model. The results showed that the prediction of GPA based on CNN model can effectively assess the groundwater distribution levels in the Pearl River Estuary island area, which can provide a certain reference value or a GPA model for the other water-deficient islands. Moreover, the obtained sample set of groundwater volume distribution of bedrock islands can be used as a valuable data source for further in-depth research.