Cited by Predicting groundwater drawdown in Zakho region, Northern Iraq, using machine learning models optimized by the whale optimization algorithm

Pollution trends and ecological risks of heavy metal(loid)s in coastal zones of Bangladesh: A chemometric review DOI

Jannatun Nahar Jannat,

Md Yousuf Mia, Most. Mastura Munia Farjana Jion

et al.

Marine Pollution Bulletin, Journal Year: 2023, Volume and Issue: 191, P. 114960 - 114960

Published: April 27, 2023

Language: Английский

Citations

Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data DOI

Rana Muhammad Adnan,

Hongliang Dai, Reham R. Mostafa

et al.

Geocarto International, Journal Year: 2022, Volume and Issue: 38(1)

Published: Dec. 14, 2022

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), jellyfish search (JFO) level estimation. Daily precipitation temperature datasets acquired from two stations northern Bangladesh were used inputs models, which evaluated based on different quantitative statistics assessed RMSE, MAE, R2, some graphical inspection methods. outcomes applications revealed that efficiency ELM models was considerably improved by using algorithms. ELM-JSO RMSE standalone model 13% for optimal precipitation, temperature, testing stage. Among implemented methods, ELM-JFO performed best estimating daily level, ELM-WOA ELM-HHO, respectively, followed it. Viability a machine method Jellyfish investigated estimation.The compared hybrid ELM-PSO, ELM-HHO data Bangladesh.The improves root mean square error inputs.

Language: Английский

Citations

Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration DOI

Sadik Mahammad, Aznarul Islam, Pravat Kumar Shit

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 47, P. 101385 - 101385

Published: April 17, 2023

The Damodar Fan Delta, West Bengal, India. depletion of groundwater resources worldwide is escalating due to its profuse demand for drinking, irrigation, domestic, and industrial uses. Overexploitation in a subtropical fan delta region with rapid population growth like the Delta India great concern sustainable mapping, monitoring, managing water resources. During 2000–2020, portrayed an increase semi-critical community development blocks, implying decline level. To this end, present study intends show level dynamics including future prediction, using machine learning algorithms based on seasonal data from 2013‐14 2020‐21 30 wells. Post-monsoon kharif rabi depicted higher fall rate compared pre-monsoon monsoon periods. Future predictions best-fit model indicated increasing trend depth levels (2025–26). extreme gradient boost regressor appeared be best model, while decision tree was worst performer. major controlling factors were decreasing rainfall abstraction increased water.

Language: Английский

Citations

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning DOI

Samuel-Soma M. Ajibade, Abdelhamid Zaïdi, Festus Vıctor Bekun

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(10), P. e20297 - e20297

Published: Sept. 19, 2023

Climate change (CC) is one of the greatest threats to human health, safety, and environment. Given its current future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) understand, mitigate, adapt CC. Therefore, this paper seeks comprehensively analyze research/publications landscape on MLCC research based published documents from Scopus. The high productivity impact has produced highly cited works categorized as science, technology, engineering arts, humanities, social sciences. most prolific author Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas Chinese Academy Sciences productive affiliation research. influential countries are United States China, which attributed funding activities National Science Foundation Natural China (NSFC), respectively. Collaboration through co-authorship in high-impact journals such Remote Sensing was also identified an important factor rate among active stakeholders researching topics worldwide. Keyword co-occurrence analysis four major hotspots/themes that describe ML techniques, potential risky sectors, remote sensing, sustainable development dynamics In conclusion, finds a significant socio-economic, environmental, impact, points increased discoveries, publications, citations near future.

Language: Английский

Citations

Harnessing Machine Learning for Assessing Climate Change Influences on Groundwater Resources: A Comprehensive Review DOI

Apoorva Bamal, Md Galal Uddin, Agnieszka I. Olbert

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e37073 - e37073

Published: Aug. 28, 2024

Language: Английский

Citations

Hydrochemical appraisal of surface water from a subtropical urban river in southwestern Bangladesh using indices, GIS, and multivariate statistical analysis DOI

Rifat Shahid Shammi, Md. Saddam Hossain, Md. Humayun Kabir

et al.

Environmental Science and Pollution Research, Journal Year: 2022, Volume and Issue: 30(2), P. 3467 - 3489

Published: Aug. 10, 2022

Language: Английский

Citations

Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh DOI

Showmitra Kumar Sarkar, Fahad Alshehri,

Shahfahad

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: March 29, 2024

Language: Английский

Citations

A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges DOI

Md. Moniruzzaman Monir,

Subaran Chandra Sarker, Abu Reza Md. Towfiqul Islam

et al.

HydroResearch, Journal Year: 2024, Volume and Issue: 7, P. 285 - 300

Published: Jan. 1, 2024

The present study aims to thoroughly review GWL depletion monitoring studies completed between 2000 and 2023 based on data-driven models GIS approaches from a global perspective. summarizes the details of reviewed papers, including location, period, time scale, key objective, input parameter, applied model, performance metrics, research gaps, limitations, rate. mean rate varied worldwide 2.9 ± 1.56 1100 33.76 mm/yr using 7.6 2.98 2046 45.27 GIS-based approaches. This assesses strength relationships various keywords analyzed co-author networks Vos-viewer. It proposes groundwater development strategy evaluated papers provide long-term solution water scarcity problem. Overall, this highlights existing gaps suggests potential future paths boost associated new knowledge increase accuracy

Language: Английский

Citations

Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin DOI

Bilel Zerouali, Nadjem Bailek, Abu Reza Md. Towfiqul Islam

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101261 - 101261

Published: June 28, 2024

Language: Английский

Citations

Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis DOI

Abu Reza Md. Towfiqul Islam, Md. Abdullah-Al Mamun, Mehedi Hasan

et al.

Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 269, P. 104480 - 104480

Published: Dec. 10, 2024

Language: Английский

Citations