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

Groundwater level forecasting with machine learning models: A review

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Hourly Water Level Forecasting in an Hydroelectric Basin Using Spatial Interpolation and Artificial Intelligence

In this work, a new hydroelectric basin modelling approach is described and applied to the Pontecosi basin, Italy. Several types of data sources were used to learn the model: a number of weather stations, satellite observations, the reanalysis dataset, and basin data. With the goal of predicting the water level of the basin, the model was composed by three cascade modules. Firstly, different spatial interpolation methods, such as Kriging, Radial Basis Function, and Natural Neighbours, were compared and applied to interpolate the weather stations data nearby the basin area to infer the main environmental variables (air temperature, air humidity, precipitation, and wind speed) in the basin area. Then, using these variables as inputs, a neural network was trained to predict the mean soil moisture concentration over the area, also to improve the low availability due to satellite orbits. Finally, a non-linear auto regressive exogenous input (NARX) model was trained to simulate the basin level with different prediction horizons, using the data from the previous modules and past basin data (water level, discharge flow rate, and turbine flow rate). Accurate predictions of the basin water level were achieved within 1 to 6 h ahead, with mean absolute errors (MAE) between 2 cm and 10 cm, respectively.

Groundwater level prediction based on a combined intelligence method for the Sifangbei landslide in the Three Gorges Reservoir Area

The monitoring and prediction of the groundwater level (GWL) significantly influence the landslide kinematics. Based on the long-term fluctuation characteristics of the GWL and the time lag of triggering factors, a dynamic prediction model of the GWL based on the Maximum information coefficient (MIC) algorithm and the long-term short-term memory (LSTM) model was proposed. The Sifangbei landslide in the Three Gorges Reservoir area (TGRA) in China, wherein eight GWL monitoring sensors were installed in different locations, was taken as a case study. The monitoring data represented that the fluctuation of the GWL has a specific time lag concerning the accumulated rainfall (AR) and the reservoir water level (RWL). In addition, there were spatial differences in the fluctuation of the GWL, which was controlled by the elevation and the micro landform. From January 19, 2015, to March 6, 2017, the measured data were used to set up the predicted models. The MIC algorithm was adopted to calculate the lag time of the GWL, the RWL, and the AR. The LSTM model is a time series prediction algorithm that can transmit historical information. The Gray wolf optimization (GWO) algorithm was used to seek the most suitable hyperparameter of the LSTM model under the specific prediction conditions. The single-factor GWO-LSTM model without considering triggering factors and the support vector machine regression (SVR) model were considered to compare the prediction results. The results indicate that the MIC-GWO-LSTM model reached the highest accuracy and improved the prediction accuracy by considering the factor selection process with the learner training process. The proposed MIC-GWO-LSTM model combines the advantages of each algorithm and effectively constructs the response relationship between the GWL fluctuation and triggering factors; it also provides a new exploration for the GWL prediction, monitoring, and early warning system in the TGRA.

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine

This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash–Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.

Landslide Displacement Prediction Combining LSTM and SVR Algorithms: A Case Study of Shengjibao Landslide from the Three Gorges Reservoir Area

Displacement predictions are essential to landslide early warning systems establishment. Most existing prediction methods are focused on finding an individual model that provides a better result. However, the limitation of generalization that is inherent in all models makes it difficult for an individual model to predict different cases accurately. In this study, a novel coupled method was proposed, combining the long short-term memory (LSTM) neural networks and support vector regression (SVR) algorithm with optimal weight. The Shengjibao landslide in the Three Gorges Reservoir area was taken as a case study. At first, the moving average method was used to decompose the cumulative displacement into two components: trend and periodic terms. Single-factor models based on LSTM neural networks and SVR algorithms were used to predict the trend terms of displacement, respectively. Multi-factors LSTM and SVR models were used to predict the periodic terms of displacement. Precipitation, reservoir water level, and previous displacement are considered as the candidate factors for inputs in the models. Additionally, ensemble models based on the SVR algorithm are used to predict the optimal weight to combine the results of the LSTM and SVR models. The results show that the LSTM models display better performance than SVR models; the ensemble model with optimal weight outperforms other models. The prediction accuracy can be further improved by also considering results from multiple models.

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.