Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 901, P. 165960 - 165960

Published: Aug. 3, 2023

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies the North of Ireland using newly developed methodologies. The results reveal significant differences between new technique "one-out, all-out" approach rating quality. found status be "good," "fair," "marginal," whereas classified as "moderate," respectively. outperformed different waterbody types, with high R2 = 1, NSE 0.99, MEF 0 values. Furthermore, final assessment methodologies had lowest uncertainty (<1 %), efficiency measures (NSE MEF) indicate that are bias-free assess at any geographic scale. this proposed effective states transitional coastal Ireland. also highlighted limitations importance updating resource management systems better protection these waterbodies. findings have implications planning other similar regions.

Language: Английский

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HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 352, P. 120091 - 120091

Published: Jan. 15, 2024

Water is a vital resource supporting broad spectrum of ecosystems and human activities. The quality river water has declined in recent years due to the discharge hazardous materials toxins. Deep learning machine have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity forecasting errors, primarily non-linear datasets hyperparameter settings. To address challenges, we developed an innovative HDTO-DeepAR approach predicting indicators. This proposed compared with standalone algorithms, including DeepAR, BiLSTM, GRU XGBoost, using performance metrics such as MAE, MSE, MAPE, NSE. NSE hybrid ranges between 0.8 0.96. Given value's proximity 1, model appears be efficient. PICP values (ranging 95% 98%) indicate that highly reliable Experimental results reveal close resemblance model's predictions actual values, providing valuable insights future trends. comparative study shows suggested surpasses all existing, well-known models.

Language: Английский

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Spatiotemporal assessment of groundwater quality and quantity using geostatistical and ensemble artificial intelligence tools

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 355, P. 120495 - 120495

Published: March 1, 2024

Language: Английский

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Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 371, P. 123094 - 123094

Published: Nov. 2, 2024

Language: Английский

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Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33082 - e33082

Published: June 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.

Language: Английский

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Novel Groundwater Quality Index (GWQI) model: A Reliable Approach for the Assessment of Groundwater

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104265 - 104265

Published: Feb. 1, 2025

Language: Английский

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Macroalgae for biomonitoring of trace elements in relation to environmental parameters and seasonality in a sub-tropical mangrove estuary

Journal of Contaminant Hydrology, Journal Year: 2023, Volume and Issue: 256, P. 104190 - 104190

Published: April 27, 2023

Language: Английский

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Ecological restoration for eutrophication mitigation in urban interconnected water bodies: Evaluation, variability and strategy

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 365, P. 121475 - 121475

Published: June 20, 2024

Language: Английский

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A machine learning approach to investigate the impact of land use land cover (LULC) changes on groundwater quality, health risks and ecological risks through GIS and response surface methodology (RSM)

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121911 - 121911

Published: July 19, 2024

Groundwater resources are enormously affected by land use cover (LULC) dynamics caused increasing urbanisation, agricultural and household discharge as a result of global population growth. This study investigates the impact decadal LULC changes in groundwater quality, human ecological health from 2009 to 2021 diverse landscape, West Bengal, India. Using quality data 479 wells 734 well 2021, recently proposed Water Pollution Index (WPI) was computed, its geospatial distribution machine learning-based 'Empirical Bayesian Kriging' (EBK) tool manifested decline water since number excellent category decreased 30.5% 28% polluted increased 44% 45%. ANOVA Friedman tests revealed statistically significant differences (p < 0.0001) year-wise parameters group comparisons for both years. Landsat 7 8 satellite images were used classify types applying learning tools years, coupled with response surface methodology (RSM) first time, which that alteration attributed changes, e.g. WPI showed positive correlation built-up areas, village-vegetation cover, lands, negative water, barren forest cover. Expansion areas 0.7%, orchards 2.3%, accompanied reduction coverage 0.6%, 2.4% croplands 1.5% drop 1% increase category. However, risks through risk index (ERI) exhibited lower reduced high-risk potential zones. highlights potentiality linking using some advanced statistical like GIS RSM better management landscape ecology.

Language: Английский

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Cultivating sustainability: a multi-assessment of groundwater quality and irrigation suitability in the arid agricultural district of Dzira (Ksour Mountains, Algeria)

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(10)

Published: Sept. 4, 2024

Language: Английский

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