A Framework to Estimation of Potential Subsidence of the Aquifer Using Algorithm Genetic

Land subsidence is often triggered by over abstraction of groundwater due to increased demands from agriculture, industry and domestic uses. This problem is investigated for Shabestar plain formulating a framework to estimate subsidence potentials and the outcome is compared with measured values of them with the lowest threshold value of 30 cm. The research puts together seven hydrogeological and geological factors affecting land subsidence, which comprise groundwater level decline, aquifer media, recharge, groundwater withdrawal, land use, aquifer thickness and fault distance. Although the framework results are acceptable, Genetic Algorithm (GA) was used to optimize weights and to improve the correlation between calculated indices and their corresponding measured subsidence values. Results confirm the improvement and show further that the southern and southeastern areas in Shabestar plain have most subsidence potentials. As such management plans are essential to meet the local demands and yet to protect land against subsidence and other adverse impacts.