Groundwater Potential Mapping using Machine Learning Approach in West Java, Indonesia DOI
Jalu Tejo Nugroho, Anugrah Indah Lestari, Budhi Gustiandi

и другие.

Groundwater for Sustainable Development, Год журнала: 2024, Номер unknown, С. 101382 - 101382

Опубликована: Ноя. 1, 2024

Язык: Английский

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach DOI Creative Commons
Md Galal Uddin, M. M. Shah Porun Rana, Mir Talas Mahammad Diganta

и другие.

Heliyon, Год журнала: 2024, Номер 10(13), С. e33082 - e33082

Опубликована: Июнь 19, 2024

Monitoring of groundwater resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization quality index (WQI) models has proven effective monitoring resources, it faced substantial criticism due to its inconsistent outcomes, prompting need more reliable assessment methods. Therefore, this study addresses concern by employing data-driven root mean squared (RMS) evaluate Bhola district near Bay Bengal, Bangladesh. To enhance reliability RMS-WQI model, research incorporated extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For GWQ, utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3-), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), iron (Fe). In terms GW concentration K, Ca Mg exceeded guideline limit collected samples. The computed scores ranged from 54.3 72.1, with an average 65.2, categorizing all sampling sites' GWQ as "fair." model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) predicting accurately. Furthermore, exhibited minimal uncertainty (<1%) WQI scores. These findings implied efficacy accurately assessing areas, that would ultimately assist regional managers strategic planners sustainable management resources.

Язык: Английский

Процитировано

8
Novel Groundwater Quality Index (GWQI) model: A Reliable Approach for the Assessment of Groundwater DOI Creative Commons
Abdul Majed Sajib, Apoorva Bamal, Mir Talas Mahammad Diganta

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104265 - 104265

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model DOI
Md Galal Uddin, Apoorva Bamal, Mir Talas Mahammad Diganta

и другие.

Alexandria Engineering Journal, Год журнала: 2025, Номер 122, С. 411 - 435

Опубликована: Март 18, 2025

Язык: Английский

Процитировано

0
A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality DOI Creative Commons
Mir Talas Mahammad Diganta, Md Galal Uddin, Tomasz Dabrowski

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 957, С. 177180 - 177180

Опубликована: Ноя. 23, 2024

Язык: Английский

Процитировано

3
Enhancing local-scale groundwater quality predictions using advanced machine learning approaches DOI
Abhimanyu Singh Yadav, Abhay Raj, Basant Yadav

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122903 - 122903

Опубликована: Окт. 15, 2024

Язык: Английский

Процитировано

0
Groundwater Potential Mapping using Machine Learning Approach in West Java, Indonesia DOI
Jalu Tejo Nugroho, Anugrah Indah Lestari, Budhi Gustiandi

и другие.

Groundwater for Sustainable Development, Год журнала: 2024, Номер unknown, С. 101382 - 101382

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0