Abstract
Drought and Immethodical ground water withdrawal in recent years has caused numerous problems such as subsidence due to falling of subsurface water table, the reduction of water quality, etc. in cities across the world. This research as a case study deals with harmful effects of subsurface water withdrawal in the city of Kerman and practical monitoring of the subsidence and makes prediction of land subsidence. The artificial neural network has been used for modeling the monitored results and prediction of future subsidence. A surveying network with more than 500 installed benchmarks in an area of 334 square kilometer has been used to measure the subsidence of the city area. Benchmarks were installed in the beginning of 2004 and were monitored at the end of 2004, 2006, and 2007. For modeling, extra data were obtained from Iranian Surveying Organization for the years before 2004. The resulting model showed that, the subsidence varies between zero and 15cm per year in different parts of the City, which depends on the subsurface-layered soils, their compressibility, and the manner of subsurface water withdrawal.
Highlights
Land subsidence has caused many difficulties around the world
The artificial neural network has been used for modeling the monitored results and prediction of future subsidence
The resulting model showed that, the subsidence varies between zero and 15cm per year in different parts of the City, which depends on the subsurface-layered soils, their compressibility, and the manner of subsurface water withdrawal
Summary
Land subsidence has caused many difficulties around the world. This phenomenon takes place by various factors such as ground settlement due to deformation and displacement of subsurface soil layers, ground water withdrawal, gas and petroleum withdrawal, subsurface excavations, mine exploitation, execution of heavy buildings and embankments, ground tectonic movement, dissolving of lime layers etc. which arise from man’s interference in nature [1]. This research showed that in recent years, the subsurface water table is fluctuated in various parts of the area due to effective factors such as drought, population growth, agriculture and industrial development, the increase of domestic absorption sewage wells etc All these factors together with extending new boring deep wells have caused more water pumping and more subsidence in the region. The factors influencing land subsidence in the City of Kerman were investigated, the fluctuation of subsurface water table was monitored extensively and the land subsidence of the city was zoned precisely Based on this information, a model was introduced for the prediction of subsidence by using artificial neural network. The output of this model in comparison with other models shows independent precise prediction of land subsidence and a reduction in calculation time
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