Journal ArticleDOI
Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia.
TLDR
The extreme gradient boosting (XGBoost) model is explored as a superior SuperLearning (SL) algorithms for Pb prediction using historical data at the Bramble and Deception Bay stations, Australia.About:
This article is published in Journal of Hazardous Materials.The article was published on 2021-02-05. It has received 69 citations till now.read more
Citations
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Journal ArticleDOI
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.
Potential bioavailability assessment, source apportionment and ecological risk of heavy metals in the sediment of Brisbane River Estuary, Australia
TL;DR: In this article, a weak acid extraction was used to mobilize the loosely bound metals in estuary sediment samples, and more than 30% of Ag, As, Ca, Cd, Co, Cu, Hg, Mn Ni, Pb and Zn were leached from the sediment showing that these metals are significantly present in the bioavailable form.
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.
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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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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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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River flow forecasting through conceptual models part I — A discussion of principles☆
J.E. Nash,J.V. Sutcliffe +1 more
TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.
Journal ArticleDOI
Sequential extraction procedure for the speciation of particulate trace metals
TL;DR: In this paper, an analytical procedure involving sequential chemicai extractions was developed for the partitioning of particulate trace metals (Cd, Co, Cu, Ni, Pb, Zn, Fe, and Mn) into five fractions: exchangeable, bound to carbonates, binding to Fe-Mn oxides and bound to organic matter.