Groundwater Prediction Using Machine-Learning Tools

Hussein, Eslam A.; Thron, Christopher; Ghaziasgar, Mehrdad; Bagula, Antoine; Vaccari, M.

doi:10.3390/a13110300

Cited by 55 publications

(13 citation statements)

References 52 publications

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“…In contrast to traditional process-based numerical modeling methods, which require large, observationally based datasets and input parameters, machine learning approaches are also increasingly being used in hydrogeological applications since they are data-driven and require less calibration than processbased models (Shen et al, 2018). For example, machine learning is being used to predict temporal groundwater dynamics (Daliakopoulos et al, 2005;Banerjee et al, 2009;Adamowski and Chan, 2011;Yoon et al, 2011;Shiri et al, 2013;Gholami et al, 2015), groundwater availability (Hussein et al, 2020), map the water table (Fienen et al, 2013;Koch et al, 2019a), model groundwater quality conditions (Winkel et al, 2011;Nolan et al, 2015;Erickson et al, 2018;Koch et al, 2019b), and strengthen traditional physical-based numerical groundwater models (Shen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

Rohde

Biswas

Housman³

et al. 2021

Front. Earth Sci.

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Groundwater dependent ecosystems (GDEs) are increasingly threatened worldwide, but the shallow groundwater resources that they are reliant upon are seldom monitored. In this study, we used satellite-based remote sensing to predict groundwater levels under groundwater dependent ecosystems across California, USA. Depth to groundwater was modelled for a 35-years period (1985–2019) within all groundwater dependent ecosystems across the state (n = 95,135). Our model was developed within Google Earth Engine using Landsat satellite imagery, climate data, and field-based groundwater data [n = 627 shallow (< 30 m) monitoring wells] as predictors in a Random Forest model. Our findings show that 1) 44% of groundwater dependent ecosystems have experienced a significant long-term (1985–2019) decline in groundwater levels compared to 28% with a significant increase; 2) groundwater level declines have intensified during the most recent two decades, with 39% of groundwater dependent ecosystems experiencing declines in the 2003–2019 period compared to 27% in the 1985–2002 period; and 3) groundwater declines are most prevalent within GDEs existing in areas of the state where sustainable groundwater management is absent. Our results indicate that declining shallow groundwater levels may be adversely impacting California’s groundwater dependent ecosystems. Particularly where groundwater levels have fallen beneath plant roots or streams thereby affecting key life processes, such as forest recruitment/succession, or hydrological processes, such as streamflow that affects aquatic habitat. In the absence of groundwater monitoring well data, our model and findings can be used to help state and local water agencies fill in data gaps of shallow groundwater conditions, evaluate potential effects on GDEs, and improve sustainable groundwater management policy in California.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

Rohde

Biswas

Housman³

et al. 2021

Front. Earth Sci.

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“…For the climatic parameters that were examined in this paper, using previous month (denoted as feature [11]) was not effective, and could even degrade predictive performance when added. However, this conclusion is not applicable to other parameters such as groundwater [7,57], which involves conditions that last over multiple months. The slight improvements seen when adding [11] to evaporation may be due to this effect.…”

Section: Discussionmentioning

confidence: 94%

“…From a practical point of view, usually the most important policy decisions involving climate require monthly predictions [7]. Relatively few studies exist which use image data to make monthly predictions [57,58]. When time scales on the order of months or longer are involved, datasets are typically much smaller than those involving shorter time scales.…”

Section: Literature Review and Scope Of The Researchmentioning

confidence: 99%

“…We transformed the image pixels to greyscale (0-255) and re-sized the images to 70 × 40 to further reduce their complexity. In view of the fact that extreme values are a common occurrence in geophysical parameter data, pixel values were regularized by replacing them with their square roots, following the example of [57,80,81]. Since our study is concerned with relative performance of different algorithms rather than absolute performance, for simplicity we did not remove over-ocean pixels, which are constant in all images and hence perfectly predicted.…”

Section: Image Pre-processingmentioning

confidence: 99%

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Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

et al. 2021

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.

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“…Literatures reveal that several researchers have been using GIS to delineate groundwater potential zones with the integration of statistical approach such as simple additive weight (SAW) and analytic hierarchy process (AHP) [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ] and, machine learning. [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] The combination of GIS and remote sensing technologies reduce the ambiguity of hydrogeological data various aspect.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Zone Mapping Using Analytical Hierarchy Process and GIS in Muga Watershed, Abay Basin, Ethiopia

Melese

Belay

2021

Global Challenges

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regions across the world. [1][2][3][4] Groundwater is an important resource contributing significantly to total annual supply. [5,6] Uncontaminated and groundwater is a crucial to sustain life on earth, particularly at the present time when water scarcity is becoming central issue in many nations across the globe. [7] The tropical and subtropical regions are severely affected with the problems related to groundwater. [8] The groundwater resources potential of Ethiopia is estimated to be about 40 billion cubic meters. [9] According to ref, [10] groundwater provides more than 90% of the water for domestic and industrial supply in Ethiopia, but a very small percentage of water is used for irrigation, which mostly generate from surface water. However, Ethiopia has also suffered frequent devastating droughts with severe ramifications comprising famine, augmented poverty, and civil unrest. [10] Overexploitation has exhausted groundwater availability noticeably and also led to land subsidence at some places. [11] To control land degradation, long-term sustainable utilization of water resources, new methods for the controlling of land and water resources are increasing in the present day. Hence, assessing and constructing groundwater potential mapping is very much indispensable for better improvement of groundwater resources and techniques for its investigation. [12][13][14][15][16][17][18][19][20][21][22] In the Abay basin, groundwater is a source of domestic water in urban and rural areas. [23] According to ref [23] inadequate public water supply is being a serious problem for the community in Muga watershed due to population growth. The practical development of the groundwater resources will have a significant effect on the improvement of the community's livelihood. Generating a groundwater potential map has a significant effect to enhance the sustainable management of groundwater resources in the study area. Thus, the present study is critical for quick identification of groundwater potential for better utility in the study watershed.Geographic Information System (GIS) is a computer based system that can be used capture, store, manipulate, analyze and present geospatial data to solve several complex and complicated problem in the environment. [24] Recently, the application of GIS is being powerful and applied to deciphering the potential areas of groundwater occurrence with cost effective Groundwater is an important resource that contributes significantly to the total annual water supply. The purpose of the present study is to assess and delineate the groundwater recharge zone using geospatial technology through an analytical hierarchal process (AHP) method in to the Muga watershed, Abay Basin. Remote sensing satellite images and the corresponding data are used for the preparation of thematic layers, viz., geology, rainfall, slope, soil, curvature, topography wetness index, elevation, drainage density, land use land cover, and lineament density of the study watershed. All thematic layers are integrated with a multicr...

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Groundwater Prediction Using Machine-Learning Tools

Cited by 55 publications

References 52 publications

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Groundwater Potential Zone Mapping Using Analytical Hierarchy Process and GIS in Muga Watershed, Abay Basin, Ethiopia

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