Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors

Marine Pollution Bulletin, Год журнала: 2012, Номер 64(11), С. 2409 - 2420

Опубликована: Авг. 25, 2012

Язык: Английский

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Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

Environmental Science and Pollution Research, Год журнала: 2022, Номер 29(32), С. 48491 - 48508

Опубликована: Фев. 22, 2022

Язык: Английский

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Pollution evaluation in streams using water quality indices: A case study from Turkey's Sapanca Lake Basin

Ecological Indicators, Год журнала: 2012, Номер 18, С. 501 - 511

Опубликована: Янв. 30, 2012

Язык: Английский

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Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Water Resources Management, Год журнала: 2018, Номер 32(7), С. 2227 - 2245

Опубликована: Фев. 8, 2018

Язык: Английский

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Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models

Artificial Intelligence Review, Год журнала: 2021, Номер 54(6), С. 4619 - 4651

Опубликована: Апрель 24, 2021

Язык: Английский

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Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria

Environmental Science and Pollution Research, Год журнала: 2023, Номер 31(41), С. 54204 - 54233

Опубликована: Фев. 1, 2023

Язык: Английский

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

Journal of Environmental Management, Год журнала: 2024, Номер 352, С. 120091 - 120091

Опубликована: Янв. 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.

Язык: Английский

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Combining AHP with GIS for assessment of irrigation water quality in Çumra irrigation district (Konya), Central Anatolia, Turkey

Environmental Earth Sciences, Год журнала: 2014, Номер 73(12), С. 8217 - 8236

Опубликована: Дек. 26, 2014

Язык: Английский

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Assessment of the quality of groundwater for drinking purposes in the Upper West and Northern regions of Ghana

SpringerPlus, Год журнала: 2016, Номер 5(1)

Опубликована: Ноя. 22, 2016

Underground water is an important natural resource serving as a reliable source of drinking for many people worldwide, especially in developing countries. quality needs to be given primary research and control attention due possible contamination. This study was therefore designed determine the physico-chemical bacteriological borehole Upper West Northern regions Ghana.The conducted seven districts Ghana (including six region one region). The bacterial load samples determined using standard microbiological methods. Physico-chemical properties including pH, total alkalinity, temperature, turbidity, true colour, dissolved solids (TDS), electrical conductivity, hardness, calcium magnesium iron, ion, chloride fluoride aluminium arsenic, ammonium ions, nitrate nitrite concentrations were determined. values obtained compared with World Health Organization (WHO) standards water.The recorded alkalinity temperature ranges 6.14-7.50, 48-240 mg/l 28.8-32.8 °C, respectively. Furthermore, mean calcium, magnesium, chloride, fluoride, aluminium, ammonium, 0.06, 22.11, 29.84, 13.97, 0.00, 0.01, 2.09 0.26 mg/l, Turbidity, TDS conductivity ranged from 0.13 105 NTU, 5 130 HU, 80.1 524 131 873 µS/cm, In addition, hardness value found 178.07 whereas respectively 55.28 122.79 mg/l. Only 14% tested positive faecal coliforms.The revealed that only few parameters above tolerable limits recommended by WHO. calls regular monitoring purification boreholes ensure good quality.

Язык: Английский

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Evaluation of Water Quality for Sustainable Agriculture in Bangladesh

Water Air & Soil Pollution, Год журнала: 2017, Номер 228(10)

Опубликована: Сен. 20, 2017

Язык: Английский

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