Analysis and Prevention of the Impacts of Metro Construction on Groundwater Environment

Dai, Chaomeng

doi:10.15666/aeer/1702_48394854

Cited by 2 publications

References 6 publications

(6 reference statements)

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Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

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Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (> 0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

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Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

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Application Research on Groundwater-level Rise and Diversion Measures in Subway Station Operation Period Based on Similar Physical Models

Guo,

Zeng,

et al. 2023

Preprint

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Blocking seepage channels in underground engineering construction can easily cause changes in groundwater level and lead to a series of problems of deterioration of the geological environment. This paper uses the physical model test system and the preparation method of the corresponding stratum similar materials to carry out the physical model test of the application of the water fluctuation and diversion measures represented by the subway station. In this way, the excavation process of the subway structure is restored and the influencing factors of the water level fluctuation are studied. Firstly, according to the similarity theory, clay-similar, sand-similar and rock-similar materials were prepared using sand, clay, blanc fixe, iron powder and other raw materials. The simulation of six test conditions under four hydraulic gradient conditions was carried out, and then the solution of water-level fluctuation was proposed and tested. Finally, the influencing factors of water-level fluctuation are analyzed. The results of the study show that the largest water-level fluctuation occurs at the monitoring points on the upstream surface. The diversion measures adopted in this paper have obvious effect on the relief of water level yong height. The measured variation of water level in this experimental environment is 0.54m.

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Analysis and Prevention of the Impacts of Metro Construction on Groundwater Environment

Cited by 2 publications

References 6 publications

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Application Research on Groundwater-level Rise and Diversion Measures in Subway Station Operation Period Based on Similar Physical Models

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