An iterative updating heuristic search strategy for groundwater contamination source identification based on an ACPSO–ELM surrogate system

• Published in: Stochastic environmental research and risk assessment Vol. 35; no. 10; pp. 2153 - 2172
• Main Authors: Wang, Han, Lu, Wenxi, Chang, Zhenbo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Artificial neural networks; Chemistry and Earth Sciences; Computational Intelligence; Computer applications; Computer Science; Computer simulation; Contamination; Earth and Environmental Science; Earth Sciences; Environment; Feedback; Groundwater; Groundwater pollution; Heuristic; Iterative methods; Learning algorithms; Machine learning; Math. Appl. in Environmental Science; Mathematical models; Original Paper; Particle swarm optimization; Physics; Probability distribution; Probability Theory and Stochastic Processes; Problem solving; Search methods; Simulation; Statistics for Engineering; Waste Water Technology; Water Management; Water Pollution Control; Iterative updating heuristic search; Feedback correction step; Groundwater contamination source identification; Surrogate system; ACPSO–ELM
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-021-01994-2

The simulation-random statistics (SRS) method is one of the effective approaches to solving groundwater contamination source identification (GCSI) challenges. In this paper, we applied a heuristic search iterative process (HSIP) based on SRS to provide interval estimation and probability distributio...