Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States

Abstract 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 framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, 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.

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MACHINE-LEARNING TO PREDICT MANGANESE AND ARSENIC IN GROUNDWATER OF THE MISSISSIPPI RIVER VALLEY ALLUVIAL AQUIFER, CENTRAL UNITED STATES

10.1130/abs/2020am-354725 ◽

2020 ◽

Author(s):

Katherine Knierim ◽

◽

James A. Kingsbury

Keyword(s):

United States ◽

Machine Learning ◽

Mississippi River ◽

Alluvial Aquifer ◽

River Valley ◽

Mississippi River Valley ◽

Arsenic In Groundwater ◽

Central United States

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Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Pharmaceuticals ◽

10.3390/ph14050472 ◽

2021 ◽

Vol 14 (5) ◽

pp. 472

Author(s):

Tyler C. Beck ◽

Kyle R. Beck ◽

Jordan Morningstar ◽

Menny M. Benjamin ◽

Russell A. Norris

Keyword(s):

United States ◽

Machine Learning ◽

Drug Interactions ◽

The United States ◽

Structural Features ◽

Physiochemical Properties ◽

Drug Dosing ◽

Therapeutic Outcomes ◽

Cyp Inhibition ◽

Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

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Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Engineering Applications of Computational Fluid Mechanics ◽

10.1080/19942060.2021.1944913 ◽

2021 ◽

Vol 15 (1) ◽

pp. 1147-1158

Author(s):

Shahab S. Band ◽

Essam Heggy ◽

Sayed M. Bateni ◽

Hojat Karami ◽

Mobina Rabiee ◽

...

Keyword(s):

Wavelet Analysis ◽

Gaussian Process ◽

Groundwater Level ◽

Gaussian Process Regression ◽

Arid Areas ◽

Groundwater Level Prediction

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Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain

Sensors ◽

10.3390/s21010046 ◽

2020 ◽

Vol 21 (1) ◽

pp. 46

Author(s):

Gangqiang Zhang ◽

Wei Zheng ◽

Wenjie Yin ◽

Weiwei Lei

Keyword(s):

Machine Learning ◽

Data Fusion ◽

Groundwater Level ◽

North China Plain ◽

North China ◽

Gradient Boosting ◽

Fusion Model ◽

The North ◽

The North China Plain ◽

Prediction Module

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

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Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

Remote Sensing ◽

10.3390/rs13050907 ◽

2021 ◽

Vol 13 (5) ◽

pp. 907

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Lukáš Vlček ◽

Robert Minařík

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Data ◽

Groundwater Level ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Sampling Strategy ◽

Peat Bog ◽

Strong Impact ◽

Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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CIKM 2020 conference report

ACM SIGWEB Newsletter ◽

10.1145/3460304.3460305 ◽

2021 ◽

pp. 1-4

Author(s):

Mathieu D'Aquin ◽

Stefan Dietze

Keyword(s):

United States ◽

Machine Learning ◽

Knowledge Management ◽

Information Retrieval ◽

Conference Report ◽

The United States ◽

Knowledge Based ◽

The World ◽

Science Conference ◽

Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

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The prediction of aquifer groundwater level based on spatial clustering approach using machine learning

Environmental Monitoring and Assessment ◽

10.1007/s10661-021-08961-y ◽

2021 ◽

Vol 193 (4) ◽

Author(s):

Hamid Kardan Moghaddam ◽

Sami Ghordoyee Milan ◽

Zahra Kayhomayoon ◽

Zahra Rahimzadeh kivi ◽

Naser Arya Azar

Keyword(s):

Machine Learning ◽

Groundwater Level ◽

Spatial Clustering ◽

Clustering Approach

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