Groundwater Level Prediction Model Research Articles

Research on Groundwater Level Prediction Model of Leizhuang Well in Hubei Province Based on BP Neural Network

Research on Groundwater Level Prediction Model of Leizhuang Well in Hubei Province Based on BP Neural Network

A simplified calculation method for high-steep soil slope stability due to groundwater rise from irrigation

The groundwater is the critical factor impacting the stability of high-steep soil slopes (HsSS) on the loess tableland. In view of the shortcomings of the current commonly used calculation methods for HsSS, based on the Rankine earth pressure, the equilibrium conditions, and the strength failure criterion, a simplified calculation method for HsSS stability is proposed considering the typical characteristic that the failure surface of the high-steep slope is a double-segment line, and the critical groundwater level of the slope of the tableland edge was determined based on the HsSS stability model. The results are consistent with slope and modified the shortcomings of the existing model, such as the relatively concentrated and smaller of the stability coefficient. The critical groundwater level prediction model was applied to the Heifangtai tableland to verify the feasibility of the prediction model and point out the three high-risk points along the Heifangtai tableland.

A hybrid coupling model of groundwater level simulation considering hydrogeological parameter: a case study of Nantong City in Eastern China

Abstract Groundwater level dynamic monitoring data have the characteristics of spatio-temporal non-smoothness and strong spatio-temporal correlation. However, the current groundwater level prediction model is insufficient to consider the spatio-temporal factors of the groundwater level and the autocorrelation of spatio-temporal series, particularly the lack of consideration of hydrogeological conditions in the actual study area. Thus, this study constructed a model based on the hydrogeological conditions and the spatio-temporal characteristics of the dynamic monitoring data of groundwater in the porous confined aquifer III in Nantong, the northern wing of the Yangtze River Delta, China. The spatial autocorrelation coefficient of the hydrogeology important parameter, permeability coefficient K, is used to optimize the distance weighting coefficient of monitoring wells obtained by the K-nearest neighbor (KNN) algorithm and then reconstruct the spatio-temporal dataset and long short-term memory (LSTM) network. A spatio-temporal groundwater level prediction model LSTM-K-KNN that introduces the spatial autocorrelation of hydrogeological parameters was constructed. The reliability and accuracy of LSTM-K-KNN, LSTM, autoregressive integrated moving average (ARIMA) model, and support vector machine (SVM) were evaluated by a cross-validation algorithm. Results showed that the prediction accuracy of LSTM-K-KNN is 19.86, 43.64, and 52.38% higher than that of the other single prediction models (LSTM, ARIMA, and SVM).

A hybrid groundwater level prediction model using signal decomposition and optimised extreme learning machine

A hybrid groundwater level prediction model using signal decomposition and optimised extreme learning machine

Groundwater Level Prediction Model Using Correlation and Difference Mechanisms Based on Boreholes Data for Sustainable Hydraulic Resource Management

Drilling data for groundwater extraction incur changes over time due to variations in hydrogeological and weather conditions. At any time, if there is a need to deploy a change in drilling operations, drilling companies keep monitoring the time-series drilling data to make sure it is not introducing any changes or new errors. Therefore, a solution is needed to predict groundwater levels (GWL) and detect a change in boreholes data to improve drilling efficiency. The proposed study presents an ensemble GWL prediction (E-GWLP) model using boosting and bagging models based on stacking techniques to predict GWL for enhancing hydraulic resource management and planning. The proposed research study consists of two modules; descriptive analysis of boreholes data and GWL prediction model using ensemble model based on stacking. First, descriptive analysis techniques, such as correlation analysis and difference mechanisms, are applied to investigate boreholes log data for extracting underlying characteristics, which is critical for enhancing hydraulic resource management. Second, an ensemble prediction model is developed based on multiple hydrological patterns using robust machine learning (ML) techniques to predict GWL for enhancing drilling efficiency and water resource management. The architecture of the proposed ensemble model involves three boosting algorithms as base models (level-0) and a bagging algorithm as a meta-model that combines the base models predictions (level-1). The base models consist of the following boosting algorithms; eXtreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB). The meta-model includes Random Forest (RF) as a bagging algorithm referred to as a level-1 model. Furthermore, different evaluation metrics are used, including mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score. The performance of the proposed E-GWLP model is compared with existing ensemble and baseline models. The experimental results reveal that the proposed model performed accurately in respect of MAE, MSE, and RMSE of 0.340, 0.564, and 0.751, respectively. The MAPE and R2 score of our proposed approach is 12.658 and 0.976, respectively, which signifies the importance of our work. Moreover, experimental results suggest that E-GWLP model is suitable for sustainable water resource management and improves reservoir engineering.

Long-Term Groundwater Level Prediction Model Based on Hybrid KNN-RF Technique

Reliable seasonal prediction of groundwater levels is not always possible when the quality and the amount of available on-site groundwater data are limited. In the present work, a hybrid K-Nearest Neighbor-Random Forest (KNN-RF) is used for the prediction of variations in groundwater levels (L) of an aquifer with the groundwater relatively close to the surface (<10 m) is proposed. First, the time-series smoothing methods are applied to improve the quality of groundwater data. Then, the ensemble K-Nearest Neighbor-Random Forest (KNN-RF) model is treated using hydro-climatic data for the prediction of variations in the levels of the groundwater tables up to three months ahead. Climatic and groundwater data collected from eastern Rwanda were used for validation of the model on a rolling window basis. Potential predictors were: the observed daily mean temperature (T), precipitation (P), and daily maximum solar radiation (S). Previous day’s precipitation P (t − 1), solar radiation S (t), temperature T (t), and groundwater level L (t) showed the highest variation in the fluctuations of the groundwater tables. The KNN-RF model presents its results in an intelligible manner. Experimental results have confirmed the high performance of the proposed model in terms of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe (NSE), and coefficient of determination (R2).

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis.

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

Method for prediction of landslide movements based on random forests

Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series consisting of 2 years of data on landslide movement, groundwater level, and precipitation gathered from the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days. Practical contributions of the developed method include the possibility of automated predictions, updated and improved on a daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.

Post Audit Analysis of a Groundwater Level Prediction Model in Developed Semiconfined Aquifer System

This paper assesses groundwater recharge under conditions of long-term groundwater pumping at the Ravnik pumping site in Croatia and analyses the groundwater level prediction model used in prior aquifer modelling. The results of model calibration revealed a very low net infiltration rate at the start of the pumping site’s operation. As the operation continued, the net infiltration rate slowly increased, while the percentage of infiltrated rainfall scaled up with increasing pumping rates. The predicted recharge of the covering aquitard amounts approximately 14–15 % of the mean annual precipitation. The aquifer recharge takes place from aquitard by seepage. A subsequent simulation of the pumping site’s operation was performed for the 9 years period on the assumption that the pumping rates and the groundwater recharge would be the same as those recorded during the final calibration years. Results show that the post audit measured levels correspond relatively well to the predicted levels and that increasing of the pumping rate causes changes in the water budget in advantage of net groundwater recharge as a consequence of spreading recharge area outside of previous model boundaries.