Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Mosavi, Amir; Hosseini, Farzaneh Sajedi; Choubin, Bahram; Abdolshahnejad, Mahsa; Gharechaee, Hamidreza; Lahijanzadeh, Ahmadreza; Dineva, Adrienn

doi:10.3390/w12102770

Cited by 71 publications

(34 citation statements)

References 84 publications

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“…Random forest (RF) is one of the machine learning models that has been considered in environmental modeling in recent years owing to its simplicity, robustness, and capacity to deal with complex data 21 . According to the authors’ knowledge, although the RF model has not been implemented to assess areas susceptible to asthma, its good performance has been proved in other environmental fields, such as groundwater potential 33 , groundwater hardness 22 , flood risk 23 , and PM 10 risk 19 .…”

Section: Introductionmentioning

confidence: 99%

Asthma-prone areas modeling using a machine learning model

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2021

Sci Rep

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Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

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Section: Introductionmentioning

confidence: 99%

Asthma-prone areas modeling using a machine learning model

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2021

Sci Rep

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“…Ensemble models, in particular, frequently outperformed single models [122]. For groundwater potential mapping, Mosavi et al [123] assessed four ensemble models, i.e., Boosted generalized additive model (GamBoost), adaptive Boosting classification trees (AdaBoost), Bagged classification and regression trees (Bagged CART), and random forest (RF), and found that the Bagging models (i.e., RF and Bagged CART) had a higher performance than the Boosting models (i.e., AdaBoost and GamBoost). This indicates that ensemble models outperformed other traditional ensemble models.…”

Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Mallick

Talukdar

Kahla

et al. 2021

Water

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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.

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“…The RNNs performed best when time-lagged memory terms are built into the model for predictions. Mosavi [17] compared the performance of two ensemble decision tree models-boosted regression trees (BRT) and random forest (RF) to predict hardness of groundwater quality. More recently, Naloufi [18] used six machine learning (ML) models, including Decision Trees, to predict E. Coli concentrations in Marne River in France.…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

Hannan

Anmala

2021

Water

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The classification of stream waters using parameters such as fecal coliforms into the classes of body contact and recreation, fishing and boating, domestic utilization, and danger itself is a significant practical problem of water quality prediction worldwide. Various statistical and causal approaches are used routinely to solve the problem from a causal modeling perspective. However, a transparent process in the form of Decision Trees is used to shed more light on the structure of input variables such as climate and land use in predicting the stream water quality in the current paper. The Decision Tree algorithms such as classification and regression tree (CART), iterative dichotomiser (ID3), random forest (RF), and ensemble methods such as bagging and boosting are applied to predict and classify the unknown stream water quality behavior from the input variables. The variants of bagging and boosting have also been looked at for more effective modeling results. Although the Random Forest, Gradient Boosting, and Extremely Randomized Tree models have been found to yield consistent classification results, DTs with Adaptive Boosting and Bagging gave the best testing accuracies out of all the attempted modeling approaches for the classification of Fecal Coliforms in the Upper Green River watershed, Kentucky, USA. Separately, a discussion of the Decision Support System (DSS) that uses Decision Tree Classifier (DTC) is provided.

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Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models

Cited by 71 publications

References 84 publications

Asthma-prone areas modeling using a machine learning model

Asthma-prone areas modeling using a machine learning model

A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

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