Hydrology
Ghazaleh Mohebbi Tafreshi; Mohammad Nakhaei; razieh Lak
Abstract
Land subsidence is a nonlinear and complex process that data-driven computational intelligence models can model it. In this study, the accuracy and efficiency of hybrid fuzzy logic gene expression planning hybrid model in estimating land subsidence risk and its factors in Varamin aquifer standardized. ...
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Land subsidence is a nonlinear and complex process that data-driven computational intelligence models can model it. In this study, the accuracy and efficiency of hybrid fuzzy logic gene expression planning hybrid model in estimating land subsidence risk and its factors in Varamin aquifer standardized. For this purpose, after selecting and gathering information from 15 factors affecting the subsidence event based on research records in the GIS environment, they were first standardized by fuzzy membership functions and then gene expression programming method was used to integrate the layers. Finally, using seven important statistical benchmarks based on radar image data, the model was validated in 4 different scenarios in input data and operators. The results showed scenario 1 with input parameters of bedrock level, Debi of pumping wells, groundwater drawdown, geology and operators, +, - ×, ÷, sqr, exp, Ln, ^ 2, ^ 3,3Rt, sin, cos, Atan, is the best model in training and testing. Accordingly, the groundwater drawdown parameter had the highest effect on land subsidence in the study area.
Mojtaba Arjomandi; Ali Saremi; Amir Pouya Sarraf; Hossien Sedghi; Mahasa Roustaei
Abstract
During recent years, groundwater exploitation and thereby decreasing hydraulic head in the compressible sedimentary aquifer which is placed in the district 19 of Tehran have been caused noticeable land subsidence. The land subsidence has been damaging the infrastructures which have been being built in ...
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During recent years, groundwater exploitation and thereby decreasing hydraulic head in the compressible sedimentary aquifer which is placed in the district 19 of Tehran have been caused noticeable land subsidence. The land subsidence has been damaging the infrastructures which have been being built in the south of Tehran Basin, especially in the district 19 of Tehran. A finite-difference groundwater flow model (MODFLOW) and a synthetic aperture radar (SAR) method have been used to estimate and predict the rate of land subsidence in this area, and help hydrogeologists manage the vital groundwater resource correctly. The data have been imported into the model, and the change of the amount of land subsidence and head have been obtained for 39 years. Then the available radar images have been processed. Afterwards, the head calibration and subsidence calibration have been done. The results of the calibrations confirmed the accuracy of the results obtained by the model. Finally, this study suggests that 118 mm of land subsidence and an 11.6 m piezometric head decline are likely to occur from 2014 until 2043.
S Angornai; H Memarian; M Shariat Panahi; M.J Bolourchi
Abstract
Land subsidence is an environmental phenomenon that involves gradual or sudden settlement of the land surface because of compaction of underground material. Groundwater withdrawal, which occurs due to excessive use of water resources, is among the most important reasons for this phenomenon. Therefore, ...
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Land subsidence is an environmental phenomenon that involves gradual or sudden settlement of the land surface because of compaction of underground material. Groundwater withdrawal, which occurs due to excessive use of water resources, is among the most important reasons for this phenomenon. Therefore, land subsidence can lead to destructive results in residential, industrial and agricultural areas. As a result, subsidence caused by excessive use of groundwater resources has occurred in many countries in the world. Tehran metropolitan plain in Iran is one of the most obvious examples, where land subsidence is happening. Although the relationship between land subsidence, groundwater level decline and changes in the physical properties of subsurface material is broadly understood, a comprehensive and precise model to predict land subsidence remains unconstrained. Land subsidence modeling is a complicated matter in geological engineering but can help to better understand subsidence and possibly prevent damages. The commonly used numerical methods for modeling land subsidence are generally based on simple assumptions, which make the model results to be associated with some errors. In this study, artificial intelligent methods such as Artificial Neural Networks (ANN) were used to propose a new method to predict land subsidence. The efficiency of this method was then tested in the South Tehran plain as a case study. We have used hydrological, geotechnical, remote sensing and ambient vibrations for site effect investigations. First, the collected data was studied statistically. Then, the delay between groundwater withdrawal and subsidence was computed by genetic algorithms using available hydrographs and GPS data in a period of 27 months. Model input parameters include changes in groundwater level, natural frequency of soil, alluvial thickness, defined geographic coordinates and time. The model output was an estimated subsidence measured by radar interferometry method. The model was built in 15 time steps using a set of data having 4 months of time difference with the data used to create the model. The comparison between the predicted (modeled) and real (measured by remote sensing) subsidence shows a good correlation, which makes the proposed model reliable.