Estimation of land subsidence using coupled particle swarm optimization and genetic algorithm: The case of Damghan aquifer

Abstract

Land subsidence, which is mainly caused by over-extraction of groundwater, is one of the most important problems in arid and semi-arid regions. In the present study, seven factors affecting the land subsidence, i.e., types of subsoil, land use, pumping, recharge, thickness of the plain aquifer, distance to the fault, and groundwater depletion were considered as input data for the ALPRIFT framework and intelligence models to map both Subsidence Vulnerability Index (SVI) and prediction of land subsidence, respectively. The hybrid of particle swarm optimization (PSO) and genetic algorithm (GA) (Hybrid PSO-GA) was then used to optimize the weights of the input layers and the estimation of the land subsidence. The capability of the PSO-GA at predictions of land subsidence compared with the typical GA model, and Gene Expression Programming (GEP). The statistical indices R2, RMSE, and MAE were used to assess the accuracy and reliability of the applied models. The results showed that the Hybrid PSO-GA model had R2, RMSE, and MAE equal to 0.91, 1.11 (cm), and 0.94 (cm), respectively. In comparison with the GA, and GEP models, the Hybrid PSO-GA model improved the prediction of land subsidence and reduced RMSE by 24.30 and 16.80%, respectively. HIGHLIGHTS Hybrid particle swarm optimization and genetic algorithm (PSO-GA) as a meta-heuristic hybrid model was suggested to estimate land subsidence.; The hybrid PSO-GA model had improved land subsidence estimation compared to GA and GEP models.; Hybrid PSO-GA, a population-based optimization method, reliably estimated land subsidence.;

Keywords

• alluvial aquifer
• artificial intelligence model
• land subsidence
• optimization