According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the chang...According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.展开更多
Groundwater environment evolution can comprehensively reflect groundwater dynamics. Based on the relationship between the groundwater system and the external environment in Jinghuiqu irrigation district, adopting the ...Groundwater environment evolution can comprehensively reflect groundwater dynamics. Based on the relationship between the groundwater system and the external environment in Jinghuiqu irrigation district, adopting the Principal Component Analysis method, variation characteristics of environmental factors including climate and human activity and their impact on groundwater were systematically analyzed. The results show that groundwater level in Jinghuiqu irrigation district has been significantly dropped in nearly 34 years;the reduction of surface water irrigation use, which reduced the amounts of groundwater recharge and destroyed the water balance, is considered as the most direct cause for falling of regional groundwater level. Besides, reduction in precipitation, increase of evaporation also accelerated the declining of the groundwater level at some extent. Finally, a predicting method of groundwater depth based on BP neural network is developed. The experimental results show that the predicting model can reasonablely predict the groundwater level in Jinghuiqu irrigation district with a high precision.展开更多
The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be ...The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be achieved by evaluating the current state of groundwater resources (GWR) exploitation, use and dynamics;setting-up, calibrating and validating the ANN;and then predicting GWLs at different lead times. The results show that GWLs in the study area have been found to reduce rapidly from 2000 to 2015, especially in the Middle-upper Pleistocene (qp2-3) and upper Pleistocene (qp3) due to the over-withdrawals from the enterprises for production purposes. Concerning this problem, an Official Letter of the People’s Committee of Can Tho City was issued and taken into enforcement in 2012 resulting in the reduction of exploitation. The calibrated ANN structures have successfully demonstrated that the GWLs can be predicted considering different impact factors. The predicted results will help to raise awareness and to draw an attention of the local/central government for a clear GWR management policy for the Mekong delta, especially the industrial zones in the urban areas such as Can Tho city.展开更多
An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple ...An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.展开更多
To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal test...To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.展开更多
准确预测地下水位是合理开发利用地下水资源的重要依据。由于数据采集过程中可能受到环境干扰或者采集装备发生故障等,导致数据缺失,进而影响模型的预测准确度。为了在含较多缺失值的地下水位数据上获得更准确的预测结果,首先,提出了基...准确预测地下水位是合理开发利用地下水资源的重要依据。由于数据采集过程中可能受到环境干扰或者采集装备发生故障等,导致数据缺失,进而影响模型的预测准确度。为了在含较多缺失值的地下水位数据上获得更准确的预测结果,首先,提出了基于长短时记忆网络(long short term memory,LSTM)-因果卷积网络(temporal convolu⁃tional network,TCN)的地下水位修复模型,通过学习原数据集中的时序特性和分布特点以改善数据集质量。然后,将多头注意力机制(multi⁃head attention mechanism,MA)与LSTM相结合进行地下水位预测,进一步提升LSTM模型的预测性能。最后,预测结果表明,使用LSTM⁃TCN方法修复后的数据集训练MA⁃LSTM模型,显著地提高了地下水位预测精度。展开更多
基金supported by the China Earthquake Administration, Institute of Seismology Foundation (IS201526246)
文摘According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.
文摘Groundwater environment evolution can comprehensively reflect groundwater dynamics. Based on the relationship between the groundwater system and the external environment in Jinghuiqu irrigation district, adopting the Principal Component Analysis method, variation characteristics of environmental factors including climate and human activity and their impact on groundwater were systematically analyzed. The results show that groundwater level in Jinghuiqu irrigation district has been significantly dropped in nearly 34 years;the reduction of surface water irrigation use, which reduced the amounts of groundwater recharge and destroyed the water balance, is considered as the most direct cause for falling of regional groundwater level. Besides, reduction in precipitation, increase of evaporation also accelerated the declining of the groundwater level at some extent. Finally, a predicting method of groundwater depth based on BP neural network is developed. The experimental results show that the predicting model can reasonablely predict the groundwater level in Jinghuiqu irrigation district with a high precision.
文摘The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be achieved by evaluating the current state of groundwater resources (GWR) exploitation, use and dynamics;setting-up, calibrating and validating the ANN;and then predicting GWLs at different lead times. The results show that GWLs in the study area have been found to reduce rapidly from 2000 to 2015, especially in the Middle-upper Pleistocene (qp2-3) and upper Pleistocene (qp3) due to the over-withdrawals from the enterprises for production purposes. Concerning this problem, an Official Letter of the People’s Committee of Can Tho City was issued and taken into enforcement in 2012 resulting in the reduction of exploitation. The calibrated ANN structures have successfully demonstrated that the GWLs can be predicted considering different impact factors. The predicted results will help to raise awareness and to draw an attention of the local/central government for a clear GWR management policy for the Mekong delta, especially the industrial zones in the urban areas such as Can Tho city.
基金the University of Tabriz through a Grant scheme No.808.
文摘An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.
基金the National Natural Science Foundation of China (Nos. 50674083 and 51074162) for its financial support
文摘To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.
文摘准确预测地下水位是合理开发利用地下水资源的重要依据。由于数据采集过程中可能受到环境干扰或者采集装备发生故障等,导致数据缺失,进而影响模型的预测准确度。为了在含较多缺失值的地下水位数据上获得更准确的预测结果,首先,提出了基于长短时记忆网络(long short term memory,LSTM)-因果卷积网络(temporal convolu⁃tional network,TCN)的地下水位修复模型,通过学习原数据集中的时序特性和分布特点以改善数据集质量。然后,将多头注意力机制(multi⁃head attention mechanism,MA)与LSTM相结合进行地下水位预测,进一步提升LSTM模型的预测性能。最后,预测结果表明,使用LSTM⁃TCN方法修复后的数据集训练MA⁃LSTM模型,显著地提高了地下水位预测精度。
