A Comparative Study of Data-driven Models for Groundwater Level Forecasting

Sarma, Riki; Singh, Santosh Kumar

doi:10.1007/s11269-022-03173-6

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…Fully understanding the variation characteristics of groundwater level is a prerequisite for rational development and utilization of groundwater resources [10][11][12]. It is indicated from many previous studies that groundwater level is affected by many factors, such as hydro-meteorological conditions, ground factors, and human activities [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Cai

Huang

Xu³

et al. 2022

Water

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Many irrigation districts along the Yellow River have been suffering shallow groundwater depression and agriculture-use water shortage. For comprehending response relationships of shallow groundwater level and various factors under different conditions of groundwater buried depth, the hydro-meteorological time series and the agricultural production data in Puyang area of Henan Province, China during 2006–2018 were collected for performing wavelet analysis of the relationship between the groundwater level and the four different factors, such as precipitation, air temperature, water stage of the Yellow River, and well irrigation water amount. It is shown that when the burial depth of groundwater varied from 0–10 m to over 10 m, the groundwater level was related with both the precipitation and air temperature from moderately to weakly and the delayed response times of the groundwater level to them extended from 2–4 months to more than 5 months. The groundwater level maintained a medium correlation with the well irrigation water amount as the burial depth increased, but the lag response time of groundwater level to well irrigation dramatically decreased when the burial depth exceeded 10 m. The dynamic response relationship between the groundwater and the water stage of the Yellow River was mainly affected by the distance away from the Yellow River rather than the burial depth and the influence of the river stage on the groundwater level was limited within the distance approximate to 20 km away from the Yellow River. The findings are expected to provide the reference for groundwater level prediction and groundwater resources protection.

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Section: Introductionmentioning

confidence: 99%

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Cai

Huang

Xu³

et al. 2022

Water

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“…This near-stationary or regular dynamics allow ANN models, and in particular recurrent neural networks such as the LSTM, to adequately predict the time series, as the output variable becomes increasingly easy to predict as the time series advances. in such conditions, it would be possible to predict the groundwater level of the well P5 by adopting only a univariate prediction approach, which relies solely on the groundwater level observed data as input to the LSTM optimized model (Raghavendra and Deka, 2016;Mohanasundaram et al, 2019;Roy et al, 2021;Sarma and Singh, 2022).…”

Section: Model Performance Assessment Of a Stationary Groundwater Levelmentioning

confidence: 99%

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

Bouramtane,

Leblanc,

Kacimi

et al. 2023

Front. Water

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The planning and management of groundwater in the absence of in situ climate data is a delicate task, particularly in arid regions where this resource is crucial for drinking water supplies and irrigation. Here the motivation is to evaluate the role of remote sensing data and Input feature selection method in the Long Short Term Memory (LSTM) neural network for predicting groundwater levels of five wells located in different hydrogeological contexts across the Oum Er-Rbia Basin (OER) in Morocco: irrigated plain, floodplain and low plateau area. As input descriptive variable, four remote sensing variables were used: the Integrated Multi-satellite Retrievals (IMERGE) Global Precipitation Measurement (GPM) precipitation, Moderate resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), MODIS land surface temperature (LST), and MODIS evapotranspiration. Three LSTM models were developed, rigorously analyzed and compared. The LSTM-XGB-GS model, was optimized using the GridsearchCV method, and uses a single remote sensing variable identified by the input feature selection method XGBoost. Another optimized LSTM model was also constructed, but uses the four remote sensing variables as input (LSTM-GS). Additionally, a standalone LSTM model was established and also incorporating the four variables as inputs. Scatter plots, violin plots, Taylor diagram and three evaluation indices were used to verify the performance of the three models. The overall result showed that the LSTM-XGB-GS model was the most successful, consistently outperforming both the LSTM-GS model and the standalone LSTM model. Its remarkable accuracy is reflected in high R2 values (0.95 to 0.99 during training, 0.72 to 0.99 during testing) and the lowest RMSE values (0.03 to 0.68 m during training, 0.02 to 0.58 m during testing) and MAE values (0.02 to 0.66 m during training, 0.02 to 0.58 m during testing). The LSTM-XGB-GS model reveals how hydrodynamics, climate, and land-use influence groundwater predictions, emphasizing correlations like irrigated land-temperature link and floodplain-NDVI-evapotranspiration interaction for improved predictions. Finally, this study demonstrates the great support that remote sensing data can provide for groundwater prediction using ANN models in conditions where in situ data are lacking.

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“…Several studies are based on the application of machine learning approaches in geosciences and related subjects [27][28][29][30][31]. Utilizing data-driven approaches toward the prediction of groundwater level is not a new phenomenon, and traditionally, numerical methods have been used for groundwater level modeling [32][33][34]. However, recent studies have extensively employed Artificial Intelligence (AI) based techniques [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Fronzi,

Narang,

Galdelli

et al. 2023

Water

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Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.

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A Comparative Study of Data-driven Models for Groundwater Level Forecasting

Cited by 14 publications

References 39 publications

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

Dynamic Response Characteristics of Shallow Groundwater Level to Hydro-Meteorological Factors and Well Irrigation Water Withdrawals under Different Conditions of Groundwater Buried Depth

The contribution of remote sensing and input feature selection for groundwater level prediction using LSTM neural networks in the Oum Er-Rbia Basin, Morocco

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

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