Journal ArticleDOI
Manganese (Mn) removal prediction using extreme gradient model.
TLDR
The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF) and outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.About:
This article is published in Ecotoxicology and Environmental Safety.The article was published on 2020-11-01. It has received 47 citations till now.read more
Citations
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An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions.
TL;DR: The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades as mentioned in this paper, and several machine learning (ML) models have been developed for modeling HMs with outstanding progress.
Journal ArticleDOI
Prediction of groundwater quality using efficient machine learning technique.
TL;DR: A deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest, eXtreme gradient boosting (XGBoost), and artificial neural network, which showed that DL model is the best prediction model with the highest accuracy.
Journal ArticleDOI
Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.
Suraj Kumar Bhagat,Tiyasha Tiyasha,Salih Muhammad Awadh,Tran Minh Tung,Ali H. Jawad,Zaher Mundher Yaseen +5 more
TL;DR: The proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.
Journal ArticleDOI
Groundwater level prediction using machine learning models: A comprehensive review
Haiyang Wang Tao,Mohammed Majeed Hameed,Haydar Abdulameer Marhoon,Mohammad Zounemat-Kermani,Heddam Salim,Kim Sungwon,Sadeq Oleiwi Sulaiman,Mou Leong Tan,Zulfaqar Sa’adi,Ali Danandeh Mehr,Mohammed Falah Allawi,Sani Isah Abba,Jasni Mohamad Zain,Mayadah W. Falah,Mehdi Jamei,Neeraj Dhanraj Bokde,M. Bayatvarkeshi,Mustafa Al-Mukhtar,Suraj Kumar Bhagat,Tiyasha Tiyasha,Khaled Mohamed Khedher,Nadhir Al-Ansari,Shamsuddin Shahid,Zaher Mundher Yaseen +23 more
TL;DR: In this article , the authors provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain, as well as recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge.
Journal ArticleDOI
Proposition of New Ensemble Data-Intelligence Models for Surface Water Quality Prediction
Ali Omran Al-Sulttani,Mustafa Al-Mukhtar,Ali B. Roomi,Aitazaz A. Farooque,Khaled Mohamed Khedher,Zaher Mundher Yaseen +5 more
TL;DR: In this paper, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM).
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.