Informed Search Strategy for Synchronous Recognition of Groundwater Pollution Sources and Aquifer Parameters Based on an Improved DCN Substitute

Abstract

An informed search strategy based on random statistical analysis was developed for synchronous recognition of groundwater pollution source information and aquifer parameters. An informed search iterative course (ISIC) was accordingly designed, and each iteration included the determination of attempt point and state transition. In this paper, two improvement techniques were first adopted for choosing attempt points and judging state transition in ISIC to improve search efficiency and precision. The first improvement was that the variable radius free search method was applied to choosing the attempt point, and the size of the search radius was constantly adjusted in ISIC, taking the search ergodicity and efficiency into account. The second improvement technique was a Tsallis formula used for state transition judgment, and the controlled factor in the Tsallis formula was regulated continuously so that the search could consider ergodicity and efficiency simultaneously. Furthermore, frequent calls to the groundwater pollution numerical simulator to calculate the likelihood have inflicted a huge computational burden during ISIC. An effective way is to construct a substitute for emulating the simulator with a low calculating load. However, the mapping relation between the import and export of the numerical simulator was complex and had many variables. The precision of the substitute based on shallow learning is low sometimes. Therefore, we adopted the deep learning method and built an improved deep confidence network (DCN) substitute to emulate the highly nonlinear simulator. Finally, the synchronous recognition results for groundwater pollution source information and aquifer parameters were gained when ISIC ceased. The above-mentioned methods were verified in a case involving groundwater pollution. The consequence indicated that the ISIC with an improved DCN substitute can synchronously recognize groundwater pollution source information and aquifer parameters with a high degree of precision and efficiency.