Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

Rahmati, Omid; Choubin, Bahram; Fathabadi, Abolhasan; Coulon, Frédéric; Soltani, Elinaz; Shahabi, Himan; Mollaefar, Eisa; Tiefenbacher, John P.; Cipullo, Sabrina; Ahmad, Baharin Bin; Bui, Dieu Tien

doi:10.1016/j.scitotenv.2019.06.320

Cited by 183 publications

(65 citation statements)

References 91 publications

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“…The PICP values for the RF NO3 model of 0.91 corresponds to p (0.90), indicating correctly assessed prediction uncertainties with QRF. Other studies on prediction of groundwater nitrate concentration determined uncertainties in a similar range (Ransom et al 2017, Rahmati et al 2019. Koch et al (2019) pointed out that the uncertainty can be significantly reduced with a more comprehensive data set.…”

Section: Spatial Distribution Of Groundwater Nitrate Concentrationmentioning

confidence: 93%

“…Due to the complex processes influencing groundwater quality, estimations are accompanied by uncertainties that need to be quantified (Refsgaard et al 2007, Grizzetti et al 2015. As one of the few, Rahmati et al (2019) investigated the uncertainties in machine learning approaches for estimating groundwater nitrate concentrations and showed that it is important to consider both the performance and the uncertainty for model evaluation. Ransom et al (2017) used bootstrapping for a boosted regression tree model and Koch et al (2019) extended RF with geostatistics to assess uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Knoll

Bach

2020

Environ. Res. Lett.

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The protection of water resources and development of mitigation strategies require large-scale information on water pollution such as nitrate. Machine learning techniques like random forest (RF) have proven their worth for estimating groundwater quality based on spatial environmental predictors. We investigate the potential of RF and quantile random forest (QRF) to estimate redox conditions and nitrate concentration in groundwater (1 km × 1 km resolution) using the European Water Framework Directive groundwater monitoring network as well as spatial environmental information available throughout Germany. The RF model for nitrate achieves a good predictive performance with an R 2 of 0.52. Dominant predictors are the redox conditions in the groundwater body, hydrogeological units and the percentage of arable land. An uncertainty assessment using QRF shows rather large uncertainties with a mean prediction interval (MPI) of 53.0 mg l −1 . This study represents the first nation-wide data-driven assessment of the spatial distribution of groundwater nitrate concentrations for Germany.

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Section: Spatial Distribution Of Groundwater Nitrate Concentrationmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Knoll

Bach

2020

Environ. Res. Lett.

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“…In recent years, with the help of advance information technology, machine learning has been introduced and applied to solve a lot of real-world problems including groundwater potential mapping [11]. Recently Pal et al [12] applied the machine learning methods namely Random Forest (RF), Radial Basis Function Classifier (RBFC) and Artificial Neural Network (ANN) to assess the capacity of the groundwater potential in the Tangon watershed in eastern Indian and Bangladesh.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

113

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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“…Machine learning techniques have recently gained good attention among the environmental modeling research community as they are advantageous in efficiently capturing the complex relationship between the environmental predictors and the response, such as flood [33][34][35][36][37][38][39][40][41], wildfire [42], sinkhole [43], drought [44], gully erosion [45,46], groundwater [47][48][49] and land/ground subsidence [27], and landslide in this case [3,13,[50][51][52][53][54][55][56][57]. In due course, researches have also attempted to improve the prediction accuracy and the interpretability of the models through applying various decision-trees machine learning algorithms such as chi-square automatic interaction detector; quick, unbiased and efficient statistical tree [58]; J48 decision trees [59]; ID3 decision trees [60]; random forests [61]; classification and regression trees [62]; alternating decision trees [63]; reduced error pruning trees [3]; naïve Bayes [35,53]; naïve Bayes tree [13,64]; kernel logistic regression [37]; logistic model tree [38,…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

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Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

Cited by 183 publications

References 91 publications

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

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