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A platform for research: civil engineering, architecture and urbanism
Artificial neural network modeling of the subsidence induced by overexploitation of groundwater in Isfahan-Borkhar Plain, Iran
https://orcid.org/0000-0001-9770-1695
Abstract Expansion of agriculture and industry has led to the growing exploitation of groundwater resources and groundwater level decline in the Isfahan-Borkhar plain. The steady decline in groundwater in susceptible areas has resulted in land subsidence. In this research, the subsidence level of the plain was measured between 2014 and 2017 using radar interferometry technique. On the other hand, the effects of groundwater level decline and aquifer parameters (e.g., alluvium thickness, fine-grained sediments percentage, hydraulic conductivity, storage coefficient, water level, and water level decline) on the subsidence were evaluated. To this end, the deformation in areas where the radar interferometry technique has failed to measure due to significant vegetation cover was estimated using an artificial neural network (ANN)-based model. This model was developed to predict the subsidence amount at any point, assuming that all parameters affecting the occurrence of this phenomenon are known. Next, the sensitivity analysis was performed using trained model parameters to determine the parameter with the highest impact on the subsidence event in the study area. The modeling results showed that the model could predict the subsidence with high accuracy. The most effective parameters in the subsidence event in this area were water level decline and fine-grained sediments percentages. Overall, comparing the subsidence level estimated from time series analysis with changes in groundwater level at some piezometers demonstrates the high impact of fine-grained sediments on the subsidence event in the area.
Artificial neural network modeling of the subsidence induced by overexploitation of groundwater in Isfahan-Borkhar Plain, Iran
https://orcid.org/0000-0001-9770-1695
Abstract Expansion of agriculture and industry has led to the growing exploitation of groundwater resources and groundwater level decline in the Isfahan-Borkhar plain. The steady decline in groundwater in susceptible areas has resulted in land subsidence. In this research, the subsidence level of the plain was measured between 2014 and 2017 using radar interferometry technique. On the other hand, the effects of groundwater level decline and aquifer parameters (e.g., alluvium thickness, fine-grained sediments percentage, hydraulic conductivity, storage coefficient, water level, and water level decline) on the subsidence were evaluated. To this end, the deformation in areas where the radar interferometry technique has failed to measure due to significant vegetation cover was estimated using an artificial neural network (ANN)-based model. This model was developed to predict the subsidence amount at any point, assuming that all parameters affecting the occurrence of this phenomenon are known. Next, the sensitivity analysis was performed using trained model parameters to determine the parameter with the highest impact on the subsidence event in the study area. The modeling results showed that the model could predict the subsidence with high accuracy. The most effective parameters in the subsidence event in this area were water level decline and fine-grained sediments percentages. Overall, comparing the subsidence level estimated from time series analysis with changes in groundwater level at some piezometers demonstrates the high impact of fine-grained sediments on the subsidence event in the area.
Artificial neural network modeling of the subsidence induced by overexploitation of groundwater in Isfahan-Borkhar Plain, Iran
https://orcid.org/0000-0001-9770-1695
Abstract Expansion of agriculture and industry has led to the growing exploitation of groundwater resources and groundwater level decline in the Isfahan-Borkhar plain. The steady decline in groundwater in susceptible areas has resulted in land subsidence. In this research, the subsidence level of the plain was measured between 2014 and 2017 using radar interferometry technique. On the other hand, the effects of groundwater level decline and aquifer parameters (e.g., alluvium thickness, fine-grained sediments percentage, hydraulic conductivity, storage coefficient, water level, and water level decline) on the subsidence were evaluated. To this end, the deformation in areas where the radar interferometry technique has failed to measure due to significant vegetation cover was estimated using an artificial neural network (ANN)-based model. This model was developed to predict the subsidence amount at any point, assuming that all parameters affecting the occurrence of this phenomenon are known. Next, the sensitivity analysis was performed using trained model parameters to determine the parameter with the highest impact on the subsidence event in the study area. The modeling results showed that the model could predict the subsidence with high accuracy. The most effective parameters in the subsidence event in this area were water level decline and fine-grained sediments percentages. Overall, comparing the subsidence level estimated from time series analysis with changes in groundwater level at some piezometers demonstrates the high impact of fine-grained sediments on the subsidence event in the area.
Artificial neural network modeling of the subsidence induced by overexploitation of groundwater in Isfahan-Borkhar Plain, Iran
Rafiee, Mohsen (author) / Ajalloeian, Rassoul (author) / Dehghani, Maryam (author) / Mahmoudpour, Masoud (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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