Investigating the artificial intelligence methods for determining performance of the NZVI permeable reactive barriers-Reference-Cited by-同舟云学术

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Investigating the artificial intelligence methods for determining performance of the NZVI permeable reactive barriers

Published:2021-02 Issue: Volume:12 Page:100516
ISSN:2352-801X
Container-title:Groundwater for Sustainable Development
language:en
Short-container-title:Groundwater for Sustainable Development

Author:

Naghikhani Ali,Jodeiri Ata,Karbassi Abdolreza,Baghdadi Majid,Sarang Amin,Buchali Safiee Amir Hosein

Publisher

Elsevier BV

Subject

Water Science and Technology,Geography, Planning and Development,Environmental Chemistry,Environmental Engineering

Reference57 articles.

1. Enhanced reduction of nitrate by zero-valent iron at elevated temperatures;Ahn;J. of hazardous materials,2008

2. Truncation errors in finite-difference models for a solute transport equation with the first-order reaction;Ataie-Ashtiani;J. Contam. Hydrol.,1999

3. Status of nano remediation and its potential for future deployment: risk-benefit and benchmarking appraisals;Bardos;J. Remediation.,2018

4. Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs;Britz,2015

5. Remediation of soil and groundwater contaminated with organic chemicals using stabilized nanoparticles: lessons from the past two decades;Cai;J. Front. Environ. Sci. Eng.,2020

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1. Deep learning algorithms in predicting Cr(VI) removal performance of S-ZVI: Models building and optimal parameters prediction;Separation and Purification Technology;2024-02

2. Tree-based ensemble machine learning model for nitrate reduction by zero-valent iron;Journal of Water Process Engineering;2023-12

3. Application of machine learning models in groundwater quality assessment and prediction: progress and challenges;Frontiers of Environmental Science & Engineering;2023-10-28

4. Investigating the sustainable performance of a nanoscale zerovalent iron permeable reactive barrier for removal of nitrate, sulfide, and arsenic;AQUA — Water Infrastructure, Ecosystems and Society;2023-03-17

5. Tree-Based Ensemble Machine Learning Model For Predictive and Interpretability Analysis of Nitrate Reduction by Zero-Valent Iron Rate Constant and Product Conversion;2023

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