Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest

He, Song; Wu, Jianhua; Wang, Dan; He, Xiaodong

doi:10.1016/j.chemosphere.2021.133388

Cited by 134 publications

(46 citation statements)

References 74 publications

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“…The cumulative result of the trees provides a reasonable prediction. The model also identifies the most significant variables that explain the dependent variable, which frequently leads to improved performance (20,21). In this study, 100 trees were used in the RF model.…”

Section: Data Preprocessing and Development Of Predictive Modelsmentioning

confidence: 99%

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Akbulut¹

2022

Erciyes Med J

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Objective:The primary aim of this study was to use metagenomic next-generation sequencing (mNGS) data to identify coronavirus 2019 (COVID-19)-related biomarker genes and to construct a machine learning model that could successfully differentiate patients with COVID-19 from healthy controls. Materials and Methods:The mNGS dataset used in the study demonstrated expression of 15,979 genes in the upper airway in 234 patients who were COVID-19 negative and COVID-19 positive. The Boruta method was used to select qualitative biomarker genes associated with COVID-19. Random forest (RF), gradient boosting tree (GBT), and multi-layer perceptron (MLP) models were used to predict COVID-19 based on the selected biomarker genes. Results:The MLP (0.936) model outperformed the GBT (0.851), and RF (0.809) models in predicting COVID-19. The three most important biomarker candidate genes associated with COVID-19 were IFI27, TPTI, and FAM83A. Conclusion:The proposed model (MLP) was able to predict COVID-19 successfully. The results showed that the generated model and selected biomarker candidate genes can be used as diagnostic models for clinical testing or potential therapeutic targets and vaccine design.

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Section: Data Preprocessing and Development Of Predictive Modelsmentioning

confidence: 99%

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Akbulut¹

2022

Erciyes Med J

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“…On the other hand, dry climate conditions with higher temperatures, evapotranspiration, and lower aridity index may lead to elevated GW NO 3 by promoting greater surficial recharge, N mineralization in soil, and nitrification by the micro-organisms . Thus, climate variables can be of high importance, and previous studies have also reported similar importance of climate variables in predicting GW NO 3 . , …”

Section: Resultsmentioning

confidence: 98%

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

et al. 2022

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One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GW NOd 3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GW NOd 3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GW NOd 3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GW NOd 3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GW NOd 3 pollution. The outcomes show that the climate factors may impose a significant influence on the GW NOd 3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

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“…Some sections are polluted severely throughout the year, and the general pollution factor is nitrate (Chen et al 2007 ; Li et al 2021 ; Liu et al 2013 ). However, the main pollutants observed in the Yellow River Basin are organic matter, nitrate and trace metals which are mainly sourced from industrial point sources and agricultural nonpoint sources (Wang et al 2021 ; He and Li 2020 ; He et al 2022a , b ), and arsenic and fluoride that are mainly of geogenic origin (He et al 2020 , 2021b ; Liu et al 2021a ; Li et al 2019 ; Wei et al 2021 ).…”

Section: Water Resources In the Yellow River Basinmentioning

confidence: 99%

Sustainable water resources development and management in large river basins: an introduction

Wang

et al. 2022

Environ Earth Sci

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Water resources are important in large basins which are important places for human habitation and industrial and agricultural development. The background of editing this thematic issue was introduced and the general water resources situation and water quality status in four major large river basins in the Asian and African continents were briefly summarized to give readers general pictures of water resources development and management in these basins, and these large river basins are the Yellow River Basin, the Yangtze River Basin, the Indus Basin, and the Nile Basin. The thematic issue papers were classified into four clustered topical categories, and the main points of the papers in this thematic issue were summarized. Finally, the perspectives of future sustainable water resources development and management in large river basins were proposed.

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Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest

Cited by 134 publications

References 74 publications

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia

Sustainable water resources development and management in large river basins: an introduction

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