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Suraj Kumar Bhagat

Bio: Suraj Kumar Bhagat is an academic researcher from Ton Duc Thang University. The author has contributed to research in topics: Computer science & Multivariate adaptive regression splines. The author has an hindex of 7, co-authored 15 publications receiving 181 citations.

Papers
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TL;DR: In this review, each element of the predictive models and their corresponding treatment processes, including its pros and cons, are discussed thoroughly and several research directions, which could bridge the gap in the same domain are proposed and recommended on the basis of the identified research limitations.

111 citations

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TL;DR: 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.

69 citations

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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.

68 citations

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TL;DR: 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.

47 citations

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TL;DR: The ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.
Abstract: An enhanced hybrid articial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting th ...

46 citations


Cited by
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Journal ArticleDOI
TL;DR: Overall, this survey provides a new milestone in water resource engineering on the AI model implementation, innovation and transformation in surface WQ modelling with many formidable problems in different blossoming area and objectives to be achieved in the future.

302 citations

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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.

128 citations

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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.

104 citations

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TL;DR: In this article , the authors review application of biochar-based for carbon sink, covering agronomy, animal farming, anaerobic digestion, composting, environmental remediation, construction, and energy storage.
Abstract: In the context of climate change and the circular economy, biochar has recently found many applications in various sectors as a versatile and recycled material. Here, we review application of biochar-based for carbon sink, covering agronomy, animal farming, anaerobic digestion, composting, environmental remediation, construction, and energy storage. The ultimate storage reservoirs for biochar are soils, civil infrastructure, and landfills. Biochar-based fertilisers, which combine traditional fertilisers with biochar as a nutrient carrier, are promising in agronomy. The use of biochar as a feed additive for animals shows benefits in terms of animal growth, gut microbiota, reduced enteric methane production, egg yield, and endo-toxicant mitigation. Biochar enhances anaerobic digestion operations, primarily for biogas generation and upgrading, performance and sustainability, and the mitigation of inhibitory impurities. In composts, biochar controls the release of greenhouse gases and enhances microbial activity. Co-composted biochar improves soil properties and enhances crop productivity. Pristine and engineered biochar can also be employed for water and soil remediation to remove pollutants. In construction, biochar can be added to cement or asphalt, thus conferring structural and functional advantages. Incorporating biochar in biocomposites improves insulation, electromagnetic radiation protection and moisture control. Finally, synthesising biochar-based materials for energy storage applications requires additional functionalisation.

94 citations

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TL;DR: This review summarizes various AI techniques and their applications in water treatment with a focus on the adsorption of pollutants and makes recommendations to ensure the successful applications of AI in future water-related technologies.

91 citations