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

Idrees Majeed Kareem,

Hindreen Mohammed Nazif

и другие.

Environmental Earth Sciences, Год журнала: 2024, Номер 83(22)

Опубликована: Ноя. 1, 2024

Язык: Английский

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

и другие.

Marine Pollution Bulletin, Год журнала: 2023, Номер 191, С. 114960 - 114960

Опубликована: Апрель 27, 2023

Язык: Английский

Процитировано

47
Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data DOI Creative Commons

Rana Muhammad Adnan,

Hongliang Dai, Reham R. Mostafa

и другие.

Geocarto International, Год журнала: 2022, Номер 38(1)

Опубликована: Дек. 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.

Язык: Английский

Процитировано

48
Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration DOI Creative Commons
Sadik Mahammad, Aznarul Islam, Pravat Kumar Shit

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2023, Номер 47, С. 101385 - 101385

Опубликована: Апрель 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.

Язык: Английский

Процитировано

25
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning DOI Creative Commons
Samuel-Soma M. Ajibade, Abdelhamid Zaïdi, Festus Vıctor Bekun

и другие.

Heliyon, Год журнала: 2023, Номер 9(10), С. e20297 - e20297

Опубликована: Сен. 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.

Язык: Английский

Процитировано

25
Harnessing Machine Learning for Assessing Climate Change Influences on Groundwater Resources: A Comprehensive Review DOI Creative Commons
Apoorva Bamal, Md Galal Uddin, Agnieszka I. Olbert

и другие.

Heliyon, Год журнала: 2024, Номер 10(17), С. e37073 - e37073

Опубликована: Авг. 28, 2024

Язык: Английский

Процитировано

6
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

и другие.

Environmental Science and Pollution Research, Год журнала: 2022, Номер 30(2), С. 3467 - 3489

Опубликована: Авг. 10, 2022

Язык: Английский

Процитировано

25
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

и другие.

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Март 29, 2024

Язык: Английский

Процитировано

5
A critical review on groundwater level depletion monitoring based on GIS and data-driven models: Global perspectives and future challenges DOI Creative Commons

Md. Moniruzzaman Monir,

Subaran Chandra Sarker, Abu Reza Md. Towfiqul Islam

и другие.

HydroResearch, Год журнала: 2024, Номер 7, С. 285 - 300

Опубликована: Янв. 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

Язык: Английский

Процитировано

5
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

и другие.

Groundwater for Sustainable Development, Год журнала: 2024, Номер 26, С. 101261 - 101261

Опубликована: Июнь 28, 2024

Язык: Английский

Процитировано

5
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

и другие.

Journal of Contaminant Hydrology, Год журнала: 2024, Номер 269, С. 104480 - 104480

Опубликована: Дек. 10, 2024

Язык: Английский

Процитировано

4