Chanjuan Tian
Research Subjects
Chanjuan Tian has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dan Liu
Water Resources Research Institute of Shandong Province, Jinan 250013, China
2 shared publications - Xuequn Chen
Water Resources Research Institute of Shandong Province, Jinan 250013, China
2 shared publications - Yawen Chang
Shandong Key Laboratory of Water Resources and Environment, Jinan 250013, China
2 shared publications - Chao Wu
Yangtze Ecology and Environment Co., Ltd., Wuhan 430014, China
1 shared publication - Dandan Xu
Water Resources Research Institute of Shandong Province, Jinan 250013, China
1 shared publication - Luyao Wang
Shandong Green View Ecological Technology Company, Jinan 250013, China
1 shared publication
Latest Publications
- Open Access
Enhancing Coastal Aquifer Characterization and Contamination Inversion with Deep Learning
Coastal aquifers are critical freshwater resources that face increasing threats from contamination and saltwater intrusion. Traditional approaches for characterizing these aquifers are challenged by complex dynamics, high-dimensional parameter spaces, and significant computational demands. This study presents an innovative method that combines an Auto-Regressive Convolutional Neural Network (AR-CNN) surrogate model with the Iterative Local Updating Ensemble Smoother (ILUES) for the joint inversion of contamination source parameters and hydraulic conductivity fields. The AR-CNN surrogate model, trained on synthetic data generated by the SEAWAT model, effectively approximates the complex input–output relationships of coastal aquifer systems, substantially reducing computational burden. The ILUES framework utilizes observational data to iteratively update model parameters. A case study involving a heterogeneous coastal aquifer with multipoint pollution sources demonstrates the efficacy of the proposed method. The results indicate that AR-CNN-ILUES successfully estimates pollution source strengths and characterizes the hydraulic conductivity field, although some limitations are observed in areas with sparse monitoring points and complex geological structures. Compared to the traditional SEAWAT-ILUES framework, the AR-CNN-ILUES approach reduces the total inversion time from approximately 70.4 h to 16.2 h, improving computational efficiency by about 77%. These findings highlight the potential of the AR-CNN-ILUES framework as a promising tool for efficient and accurate characterization of coastal aquifers. By enhancing computational efficiency without significantly compromising accuracy, this method offers a viable solution for the sustainable management and protection of coastal groundwater resources.
Open AccessEnhancing Coastal Aquifer Characterization and Contamination Inversion with Deep Learning
Coastal aquifers are critical freshwater resources that face increasing threats from contamination and saltwater intrusion. Traditional approaches for characterizing these aquifers are challenged by complex dynamics, high-dimensional parameter spaces, and significant computational demands. This study presents an innovative method that combines an Auto-Regressive Convolutional Neural Network (AR-CNN) surrogate model with the Iterative Local Updating Ensemble Smoother (ILUES) for the joint inversion of contamination source parameters and hydraulic conductivity fields. The AR-CNN surrogate model, trained on synthetic data generated by the SEAWAT model, effectively approximates the complex input–output relationships of coastal aquifer systems, substantially reducing computational burden. The ILUES framework utilizes observational data to iteratively update model parameters. A case study involving a heterogeneous coastal aquifer with multipoint pollution sources demonstrates the efficacy of the proposed method. The results indicate that AR-CNN-ILUES successfully estimates pollution source strengths and characterizes the hydraulic conductivity field, although some limitations are observed in areas with sparse monitoring points and complex geological structures. Compared to the traditional SEAWAT-ILUES framework, the AR-CNN-ILUES approach reduces the total inversion time from approximately 70.4 h to 16.2 h, improving computational efficiency by about 77%. These findings highlight the potential of the AR-CNN-ILUES framework as a promising tool for efficient and accurate characterization of coastal aquifers. By enhancing computational efficiency without significantly compromising accuracy, this method offers a viable solution for the sustainable management and protection of coastal groundwater resources.
- Open Access
Study on the Control of Saltwater Intrusion Using Subsurface Dams
Subsurface dams are widely used to prevent saltwater intrusion, with good results. This blockage often leads to an accumulation of pollutants and salt on the inland and seaside of the dam, respectively. While the latter is intended, the former effect is not desired and poses a huge problem in groundwater management. In order to quantitatively address this issue and clarify the impact of subsurface dam height, location, and the head difference for the saltwater and freshwater boundary on saltwater wedges and fresh groundwater discharge, a flow tank and numerical model were constructed. The results indicate that there was an optimal effective dam height and location (also the minimum effective dam location) for controlling saltwater intrusion, which corresponded to the maximum groundwater and freshwater discharge. When the various conditions of the numerical model were set according to the flow tank and the dam was 15 cm away from the saltwater boundary, the minimum effective dam height was equal to the aquifer thickness multiplied by 0.36. The dam height reached a height that was slightly higher than the minimum effective height, namely, the ratio of dam height to aquifer thickness was 0.38, which revealed that the freshwater discharge reached its maximum at 22.71 cm3/min, the saline water wedge area was the smallest at 378 cm2, and the prevention effect of saltwater intrusion was the best. Building a dam too high, that was, the ratio of dam height to aquifer thickness exceeded 0.38, resulted in an increased saltwater wedge area and exacerbated aquifer pollution. When the dam was located at the minimum effective distance for preventing saltwater intrusion under a certain dam height and head difference between saltwater and freshwater boundary, that was, the ratio of the distance of the dam to the saltwater boundary to the total length of the aquifer was 0.063, the distance of the dam to the saltwater boundary was the minimum effective distance. Compared to other effective distances, when the dam was at the minimum effective distance, the freshwater discharge reached its maximum at 22.71 cm3/min, and the saltwater wedge area was the smallest at 378 cm2. These conclusions provide a theoretical reference for the impact of subsurface dam construction on the saltwater wedge. This study examines the impact of tides and waves on the water head of the saltwater boundary, and it is also necessary to verify these conclusions through actual field experiments. We will investigate this in future work.
Open AccessStudy on the Control of Saltwater Intrusion Using Subsurface Dams
Subsurface dams are widely used to prevent saltwater intrusion, with good results. This blockage often leads to an accumulation of pollutants and salt on the inland and seaside of the dam, respectively. While the latter is intended, the former effect is not desired and poses a huge problem in groundwater management. In order to quantitatively address this issue and clarify the impact of subsurface dam height, location, and the head difference for the saltwater and freshwater boundary on saltwater wedges and fresh groundwater discharge, a flow tank and numerical model were constructed. The results indicate that there was an optimal effective dam height and location (also the minimum effective dam location) for controlling saltwater intrusion, which corresponded to the maximum groundwater and freshwater discharge. When the various conditions of the numerical model were set according to the flow tank and the dam was 15 cm away from the saltwater boundary, the minimum effective dam height was equal to the aquifer thickness multiplied by 0.36. The dam height reached a height that was slightly higher than the minimum effective height, namely, the ratio of dam height to aquifer thickness was 0.38, which revealed that the freshwater discharge reached its maximum at 22.71 cm3/min, the saline water wedge area was the smallest at 378 cm2, and the prevention effect of saltwater intrusion was the best. Building a dam too high, that was, the ratio of dam height to aquifer thickness exceeded 0.38, resulted in an increased saltwater wedge area and exacerbated aquifer pollution. When the dam was located at the minimum effective distance for preventing saltwater intrusion under a certain dam height and head difference between saltwater and freshwater boundary, that was, the ratio of the distance of the dam to the saltwater boundary to the total length of the aquifer was 0.063, the distance of the dam to the saltwater boundary was the minimum effective distance. Compared to other effective distances, when the dam was at the minimum effective distance, the freshwater discharge reached its maximum at 22.71 cm3/min, and the saltwater wedge area was the smallest at 378 cm2. These conclusions provide a theoretical reference for the impact of subsurface dam construction on the saltwater wedge. This study examines the impact of tides and waves on the water head of the saltwater boundary, and it is also necessary to verify these conclusions through actual field experiments. We will investigate this in future work.