Groundwater Level Prediction Research Articles

Assessment of wavelet-SVR and wavelet-GP models in predicting the groundwater level using areal precipitation and consumption data

ABSTRACT Assessing and predicting groundwater level (GWL) fluctuations using specific models provides valuable information for water resources management and consumption planning. In this study, areal monthly data of GWL precipitation and consumptions were used to predict the GWL using genetic programming (GP), wavelet-GP, support vector regression (SVR) and wavelet-SVR models over a period of 11 years. Appropriate time lags were determined using the autocorrelation function (ACF) and cross-correlation function (CCF). It was found that consumption with a two-month lag and precipitation with a one-month lag have the greatest effect on the GWL. Discrete wavelet transform (DWT) was used to decompose time series into low- and high-frequency components for wavelet-SVR and wavelet-GP models. Two types of input structures were used for modelling (single GWL data and GWL considering consumptions and precipitation data). The results showed that the wavelet-SVR model had the best performance (according to R, RMSE and NSE) compared to the other three models, and SVR had better performance than the GP.

Prediction of groundwater-level using novel SVM-ALO, SVM-FOA, and SVM-FFA algorithms at Purba-Medinipur, India

Prediction of groundwater-level using novel SVM-ALO, SVM-FOA, and SVM-FFA algorithms at Purba-Medinipur, India

Simulation of regional groundwater levels in arid regions using interpretable machine learning models

Regional groundwater level forecasting is critical to water resource management, especially for arid regions which require effective management of groundwater resources to meet human and ecosystem needs. In this study Machine Learning and Deep Learning approaches - Support Vector Machine, Generalized Regression Neural Network, Decision Tree, Random Forest (RF), Convolutional Neural Network, Long Short Term Memory and Gated Recurrent Network- have been used to simulate the groundwater levels in the lower Tarim River basin (LTRB) which is an extreme dryland. The results showed that models developed here with easily available input data such as relative humidity, flow volume and distance to the riverbank can fully utilize spatiotemporally inconsistent groundwater monitoring data to predict the spatiotemporal variation of the groundwater system in arid regions where exist intermittent flow. The shapely additive explanations method was used to interpret the RF model and discover the effect of meteorological, hydrological and environmental variables on the regional groundwater. These explanations showed that the flow volume, the distance to the river channel and reservoir have a critical impact on groundwater changes. Within 300 m distance to the riverbank, groundwater would mainly be influenced by the flow volume and the distance to the reservoir. While far from the riverbank, these effects decreased gradually further away from the river course. The RF prediction results showed that in the next three years (2021−2023), the groundwater level on average may decline to −6.4 m, and the suitable areas for natural vegetation growth would be limited to 39% if no water conveyance in the LTRB. To guarantee the stability of ecosystems in the LTRB, it is necessary to convey the water annually. These results can support spatiotemporal predictions of groundwater levels predominantly recharged by intermittent flow, and form a scientific basis for sustainable water resources management in arid regions.

Uncertainty assessment of LSTM based groundwater level predictions

ABSTRACT Due to the underlying uncertainty in groundwater level (GWL) modelling, point prediction of GWLs does not provide sufficient information. Moreover, the insufficiency of data on subjects such as illegal exploitation wells and wastewater pounds, which are untraceable, underlines the importance of evolved uncertainty in the groundwaters of the Ardabil plain. Thus, estimating prediction intervals (PIs) for groundwater modelling can be an important step. In this paper, PIs were estimated for GWLs of selected piezometers of the Ardebil plain in Iran using the artificial neural network (ANN)-based lower upper bound estimation (LUBE) method. The classic feedforward neural network (FFNN) and deep-learning-based long short-term memory (LSTM) were used. GWL data of piezometers and hydrological data (1992–2018) were applied for modelling. The results indicate that LSTM outperforms FFNN in both PI and point prediction tasks. LSTM-based LUBE was found to be superior to FFNN-based LUBE, providing an average 25% lower coverage width criterion (CWC). PIs estimated for piezometers with high transmissivity resulted in 50% lower CWC than PIs estimated for piezometers in areas with lower transmissivity.

Groundwater level prediction using machine learning models: A comprehensive review

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.

Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level

The groundwater resources are the essential sources for irrigation and agriculture management. Forecasting groundwater levels (GWL) for the current and future periods is an essential topic of watershed management. The prediction of GWL helps prevent overexploitation. The Auto-Regressive Integrated Moving Average model (ARIMA) is a widely known linear statistical model. One of the drawbacks of the ARIMA models is that they may not capture all existing patterns, such as non-linear parts of time series. This article introduces a new hybrid model, namely the ARIMA-Long Short-Term Memory (LSTM) neural network, to capture the linear and non-linear components of a GWL time series in the Yazd-Ardekn Plain in Iran. This study applied the ARIMA-LSTM in forecasting three-, six-, and nine-month-ahead GWL. To determine the hyperparameters of the LSTM algorithm, the Salp Swarm Algorithm (SSA), sine cosine optimisation algorithm (SCOA), particle swarm optimisation algorithm (PSOA), and genetic algorithm (GA) were coupled with the LSTM model. Two different scenarios were devised to introduce new input combinations. In the first scenario, the residual values of the ARIMA model and the lagged GWL data were inserted into hybrid and standalone LSTM models for forecasting the GWL. In the second scenario, the summation of the outputs of the ARIMA and LSTM models gave the final outputs. In terms of the content of three-month-ahead GWL predictions for the second scenario, the ARIMA-LSTM-SSA produced better results than the ARIMA-LSTM-SCOA, ARIMA-LSTM-PSOA, ARIMA-LSTM-GA, ATIMA-LSTM, LSTM, and ARIMA algorithms, which had lower mean absolute error values (MAE) of 5%, 9.4%, 15%, 38%, 42%, and 47%, respectively. However, the general results indicated that an increased forecasting horizon reduced the accuracy of the models. The new hybrid ARIMA-LSTM- SSA model was highly capable of forecasting other hydrological variables for capturing non-linear and linear elements of the time series.

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

Groundwater level prediction using machine learning algorithms in a drought-prone area

Groundwater level prediction using machine learning algorithms in a drought-prone area

A Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods Improves the Prediction of Groundwater Level

Groundwater is a crucial source of water supply in drought conditions, and an auxiliary water source in wet seasons. Due to its increasing importance in view of climate change, predicting groundwater level (GWL) needs to be improved to enhance management. We used adaptive neuro-fuzzy inference systems (ANFIS) to predict the GWL of the Urmia aquifer in northwestern Iran under various input scenarios using precipitation, temperature, groundwater withdrawal, GWL during the previous month, and river flow. In total, 11 input patterns from various combinations of variables were developed. About 70% of the data were used to train the models, while the rest were used for validation. In a second step, several metaheuristic algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were used to improve the model and, consequently, prediction performance. The results showed that (i) RMSE, MAPE, and NSE of 0.51 m, 0.00037 m, and 0.86, respectively, were obtained for the ANFIS model using all input variables, indicating a rather poor performance, (ii) metaheuristic algorithms were able to optimize the parameters of the ANFIS model in predicting GWL, (iii) the input pattern that included all input variables resulted in the most appropriate performance with RMSE, MAPE, and NSE of 0.28 m, 0.00019 m, and 0.97, respectively, using the ANIFS-ACOR hybrid model, (iv) results of Taylor’s diagram (CC = 0.98, STD = 0.2, and RMSD = 0.30), as well as the scatterplot (R2 = 0.97), showed that best prediction was achieved by ANFIS-ACOR, and (v) temperature and evaporation exerted stronger influence on GWL prediction than groundwater withdrawal and precipitation. The findings of this study reveal that metaheuristic algorithms can significantly improve the performance of the ANFIS model in predicting GWL.

Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.

Can ensemble machine learning be used to predict the groundwater level dynamics of farmland under future climate: a 10-year study on Huaibei Plain.

Accurate and simple prediction of farmland groundwater level (GWL) is an important aspect of agricultural water management. A farmland GWL prediction model, GWPRE, was developed that integrates four machine learning (ML) models (support vector machine regression, random forest, multiple perceptions, and the stacking ensemble model) with weather forecasts. Based on the GWL and meteorological data of five monitoring wells (N1, N2, N3, N4, and N5) in Huaibei plain from 2010 to 2020, the feasibility of predicting GWL by meteorological factors and ML algorithm was tested. In addition, the stacking ensemble model and future meteorological data after Bayesian model averaging were introduced for the first time to predict GWL under future climate conditions. The results showed that GWL showed an increasing trend in the past decade, but it will decrease in the future. The performance of the stacking ensemble model was better than that of any single ML model, with RMSE reduced by 4.26 ~ 96.97% and the running time reduced by 49.25 ~ 99.40%. GWL was most sensitive to rainfall, and the sensitivity index ranged from 0.2547 to 0.4039. The fluctuation range of GWL of N1, N2, and N3 was 1.5 ~ 2.5m in the next decade. Due to the possible high rainfall, the GWL decreased in 2024 under RCP 2.6 and 2026 under RCP 8.5. It is worth noting that although the stacking ensemble model can improve the accuracy, it is not always the best among ML models in terms of portability. Nevertheless, the stacking ensemble model was recommended for GWL prediction under climate change.

Deep learning-based uncertainty quantification of groundwater level predictions

Deep learning-based uncertainty quantification of groundwater level predictions

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model.

The estimation of qualitative and quantitative groundwater parameters is an essential task. In this regard, artificial intelligence (AI) techniques are extensively utilized as accurate, trustworthy, and cost-effective tools. In the present paper, two hybrid neuro-fuzzy models are implemented for the prediction of groundwater level (GWL) fluctuations, as well as variations of Cl - and HCO3 - in the Karnachi well, Kermanshah, Iran in monthly intervals within a 13-year period from 2005 to 2018. In order to develop AI models, the adaptive neuro-fuzzy inference system (ANFIS), firefly algorithm (FA), and wavelet transform (WT) are used. In other words, two hybrid models including ANFIS-FA (adaptive neuro-fuzzy inference system-firefly algorithm) and WANFIS-FA (wavelet-adaptive neuro-fuzzy inference system-firefly algorithm) are utilized for the estimation of the quantitative and qualitative parameters. Firstly, influencing lags of the time-series of the qualitative and quantitative parameters are identified using the autocorrelation function. Then, four and eight separate models are developed for the approximation of GWLs and qualitative parameters (i.e. Cl - and HCO3 -), respectively. It is worth to mention that about 75% of observed values are assigned to train the hybrid AI models, while the rest (i.e. 25%) to test them. Sensitivity analysis results reveal that the WANFIS-FA models display more acceptable performance than the ANFIS-FA ones. Also, the estimations of MAE, NSC, and SI for the simulation of HCO3 - by the superior model of the WANFIS-FA are obtained to be 0.040, 0.988, and 0.022, respectively. In addition, the lags (t-1), (t-2), (t-3), and (t-4) are ascertained as the most effective time-series lags for the estimation of Cl - .

Machine Learning Applications in Agriculture

Agriculture plays a vital role in the economic growth of the country. To meet out the food requirement of the increase of population is a challenging task with frequent changes in climatic conditions and limited resources. Smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine has the ability to learn without being explicitly programmed. Machine Learning together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. Machine Learning applications in the field of agriculture are explained in this article. The areas that are focused are prediction of soil parameters such as moisture content, crop yield prediction, disease and weed detection in crops, Identify water stress in plant, Crop mapping , Crop selection prediction , Ground water level prediction: Groundwater is the largest storage of freshwater resources, which serves as the and species detection. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also discussed to reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

Mathematical model for prediction of groundwater levels in two-layer formations

A article discusses the process offorecasting changes in the level of ground and pressure water. A brief analysis and computational experiments of scientific papers on mathematical and numerical modeling of the object under study are given. For a comprehensive study of the problem under consideration, a mathematical model was developed that takes into account the external source, evaporation, filtration coefficients, active porosity, filtration rate and two-way boundary conditions. An effective numerical algorithm has been developed for predicting changes in the ground water level using a combination of finite-difference schemes and run-through methods. It has been studied that changes in the level of ground and pressure water, filtration permeability, water loss coefficient and filtration rate associated with the water level can have a serious impact on the environmental process.

An application of transfer function noise models for predicting groundwater level signals using rainfall signals in Adyar Basin, India

The effect of rainfall on groundwater level fluctuation was analysed using autoregressive moving average exogeneous (ARMAX) transfer function noise (TFN) modelling approach on four selected observation wells in Adyar Basin, Tamil Nadu, India. The evaluation of spatial average rainfall representation in the rainfall-groundwater level rise regression analysis was compared with three different methods namely simple arithmetic average (SAA), Thiessen polygon (TP), and Thiessen zone wise rainfall (TZR). Linear regression analysis on rainfall-groundwater level rise datasets revealed that the TZR method of inputting spatial average rainfall improved the rainfall-groundwater level rise correlation over SAA and TP methods. The four selected wells groundwater level fluctuation data from Adyar Basin was further modelled using ARMAX-TFN modelling approach and MLR methods. The study results show that the ARMAX-TFN model prediction performance was superior over MLR methods at all four locations. The validation results of the ARMAX-TFN model show that the predicted and observed groundwater levels at the corresponding well locations were strongly correlated with the correlation coefficient values of 0.85-0.93.

An application of transfer function noise models for predicting groundwater level signals using rainfall signals in Adyar Basin, India

An application of transfer function noise models for predicting groundwater level signals using rainfall signals in Adyar Basin, India

Groundwater level prediction of Varanasi wells during pre-monsoon and post-monsoon using intelligence approach

Groundwater level prediction of Varanasi wells during pre-monsoon and post-monsoon using intelligence approach

Hydrological Evaluation of Meandering River Restoration in Kushiro Wetland Using a Long Short-Term Memory (Lstm)Based Model for Groundwater Level Prediction

Hydrological Evaluation of Meandering River Restoration in Kushiro Wetland Using a Long Short-Term Memory (Lstm)Based Model for Groundwater Level Prediction