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Serdar Gündoğdu. (2022) Application of MLR-PRN model for estimation of arsenic concentration in drinking water: a case study for İzmir City. Urban Water Journal 19:6, pages 589-599.
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Articles from other publishers (24)
Wei Zhang, Waqar Muhammad Ashraf, Sachini Supunsala Senadheera, Daniel S. Alessi, Filip M.G. Tack & Yong Sik Ok. (2023) Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars. Science of The Total Environment 904, pages 166678.
Crossref
Crossref
Abhijit Mukherjee, Soumyajit Sarkar, Poulomee Coomar & Prosun Bhattacharya. (2023) Towards clean water: Managing risk of arsenic-contaminated groundwater for human consumption. Current Opinion in Environmental Science & Health 36, pages 100509.
Crossref
Crossref
B. Ibrahim, A. Ewusi, Y. Y. Ziggah & I. Ahenkorah. (2023) A new implementation of stacked generalisation approach for modelling arsenic concentration in multiple water sources. International Journal of Environmental Science and Technology.
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Crossref
Wenjing Guo, Zhipeng Gao, Huaming Guo & Wengeng Cao. (2023) Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models. Science of The Total Environment 897, pages 165511.
Crossref
Crossref
Ruohan Wu & David A. Polya. (2023) Objective Cost-Informed Cutoff Criteria Improve the Utility of Machine Learning Models of Environmental Hazards: A Case Study of Groundwater Arsenic Distribution in India. Exposure and Health.
Crossref
Crossref
M. Mousavi, F. Qaderi & A. Ahmadi. (2023) Spatial prediction of temporary and permanent hardness concentrations in groundwater based on chemistry parameters by artificial intelligence. International Journal of Environmental Science and Technology 20:6, pages 6665-6684.
Crossref
Crossref
Ryan Haggerty, Jianxin Sun, Hongfeng Yu & Yusong Li. (2023) Application of machine learning in groundwater quality modeling - A comprehensive review. Water Research 233, pages 119745.
Crossref
Crossref
Yanpeng Huang, Longzhen Ding, Weijiang Liu, Haobo Niu, Mengxi Yang, Guangfeng Lyu, Sijie Lin & Qing Hu. (2023) Groundwater Contamination Site Identification Based on Machine Learning: A Case Study of Gas Stations in China. Water 15:7, pages 1326.
Crossref
Crossref
Santanu Mallik, Abhigyan Chakraborty, Umesh Mishra & Niladri Paul. (2022) Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India. Environmental Science and Pollution Research 30:55, pages 116522-116537.
Crossref
Crossref
Bemah Ibrahim, Anthony Ewusi, Isaac Ahenkorah & Yao Yevenyo Ziggah. (2022) Modelling of arsenic concentration in multiple water sources: A comparison of different machine learning methods. Groundwater for Sustainable Development 17, pages 100745.
Crossref
Crossref
Maziar Mohammadi, Seyed Amir Naghibi, Alireza Motevalli & Hossein Hashemi. (2022) Human-induced arsenic pollution modeling in surface waters - An integrated approach using machine learning algorithms and environmental factors. Journal of Environmental Management 305, pages 114347.
Crossref
Crossref
Sushant K. Singh, Robert W. Taylor, Biswajeet Pradhan, Ataollah Shirzadi & Binh Thai Pham. (2022) Predicting sustainable arsenic mitigation using machine learning techniques. Ecotoxicology and Environmental Safety 232, pages 113271.
Crossref
Crossref
Mohammad Zounemat‐Kermani, Amin Mahdavi‐Meymand, Marzieh Fadaee, Okke Batelaan & Reinhard Hinkelmann. (2021) Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models. Environmental Quality Management 31:3, pages 241-251.
Crossref
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Xin Zhao, Rongjie Liu, Yi Ma, Yanfang Xiao, Jing Ding, Jianqiang Liu & Quanbin Wang. (2021) Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network. Remote Sensing 14:1, pages 88.
Crossref
Crossref
Adoubi Vincent De Paul Adombi, Romain Chesnaux & Marie-Amélie Boucher. (2021) Review: Theory-guided machine learning applied to hydrogeology—state of the art, opportunities and future challengesRevue: Apprentissage automatique guidée par la théorie appliqué à l’hydrogéologie—état de l’art, opportunités et défisRevisión: La teoría del aprendizaje automático aplicada a la hidrogeología—estado del arte, oportunidades y desafíos futuros应用于水文地质的具有理论的机器学习综述:最新技术、机遇和未来挑战Revisão: Aprendizagem de máquina orientada pela teoria aplicada à hidrogeologia—estado da arte, oportunidades e futuros desafios. Hydrogeology Journal 29:8, pages 2671-2683.
Crossref
Crossref
Liang He, Manqing Hou, Suozhong Chen, Junru Zhang, Junyi Chen & Hui Qi. (2021) Construction of a spatio-temporal coupling model for groundwater level prediction: a case study of Changwu area, Yangtze River Delta region of China. Water Supply 21:7, pages 3790-3809.
Crossref
Crossref
Khabat Khosravi, Rahim Barzegar, Ali Golkarian, Gianluigi Busico, Emilio Cuoco, Micòl Mastrocicco, Nicolò Colombani, Dario Tedesco, Maria Margarita Ntona & Nerantzis Kazakis. (2021) Predictive modeling of selected trace elements in groundwater using hybrid algorithms of iterative classifier optimizer. Journal of Contaminant Hydrology 242, pages 103849.
Crossref
Crossref
Sushant K. Singh, Ataollah Shirzadi & Binh Thai Pham. 2021. Water Pollution and Management Practices. Water Pollution and Management Practices
71
105
.
Sobia Iftikhar, Sania Bhatti, Zulfiqar Ali Bhatti, Mohsin Ali Memon & Faisal Memon. (2020) Groundwater Arsenic and Cancer Risk Assessment Prediction model via Machine Learning: A Step Towards Modernizing Academic Research. Sir Syed University Research Journal of Engineering & Technology 10:2.
Crossref
Crossref
Nico Dalla Libera, Daniele Pedretti, Fabio Tateo, Leonardo Mason, Leonardo Piccinini & Paolo Fabbri. (2020) Conceptual Model of Arsenic Mobility in the Shallow Alluvial Aquifers Near Venice (Italy) Elucidated Through Machine Learning and Geochemical Modeling. Water Resources Research 56:9.
Crossref
Crossref
Zhen Tan, Qiang YangYan Zheng. (2020) Machine Learning Models of Groundwater Arsenic Spatial Distribution in Bangladesh: Influence of Holocene Sediment Depositional History. Environmental Science & Technology 54:15, pages 9454-9463.
Crossref
Crossref
JongCheol Pyo, Hongtao Duan, Mayzonee Ligaray, Minjeong Kim, Sangsoo Baek, Yong Sung Kwon, Hyuk Lee, Taegu Kang, Kyunghyun Kim, YoonKyung Cha & Kyung Hwa Cho. (2020) An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery. Remote Sensing 12:7, pages 1073.
Crossref
Crossref
Yong Kown, Seung Baek, Young Lim, JongCheol Pyo, Mayzonee Ligaray, Yongeun Park & Kyung Cho. (2018) Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models. Water 10:8, pages 1020.
Crossref
Crossref
Kitirote Wantala, Chatkamol Keawbuddee, Suthipong Sthiannopkao & Penradee Chanpiwat. (2018) Optimization and mechanisms of As(III) removal from aqueous solution using Fe-MCM-41 immobilized on GAC. Journal of Environmental Chemical Engineering 6:2, pages 2191-2199.
Crossref
Crossref