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 the 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., the types of subsoil, land use, pumping, recharge, the 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 and prediction of land subsidence. 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 the prediction of land subsidence was compared with the typical GA model and gene expression programming (GEP). The statistical indices coefficient of correlation (R2), root mean square error (RMSE), and mean absolute error (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.