Hydro-chemical assessment of fluoride and nitrate in groundwater from east and west coasts of Bangladesh and India

Journal of Cleaner Production, Год журнала: 2022, Номер 372, С. 133675 - 133675

Опубликована: Авг. 24, 2022

Язык: Английский

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Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm

Hydrology, Год журнала: 2023, Номер 10(3), С. 58 - 58

Опубликована: Фев. 27, 2023

Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, standardized precipitation index (SPI) was monitored predicted Peru between 1990 2015. The study proposed a hybrid model, called ANN-FA, for SPI time scales (SPI3, SPI6, SPI18, SPI24). A state-of-the-art firefly algorithm (FA) has been documented as powerful tool to support modeling issues. ANN-FA uses an artificial neural network (ANN) which is coupled with FA Lima via other stations. Through intelligent utilization series from neighbors’ stations model inputs, suggested approach might be used forecast at meteorological station insufficient data. To conduct this, SPI3, SPI24 were modeled using stations’ datasets Peru. Various error criteria employed investigate performance model. Results showed that effective promising drought also multi-station strategy lack results can help predict mean absolute = 0.22, root square 0.29, Pearson correlation coefficient 0.94, agreement 0.97 testing phase best estimation (SPI3).

Язык: Английский

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Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation

Composite Structures, Год журнала: 2022, Номер 306, С. 116599 - 116599

Опубликована: Дек. 15, 2022

Язык: Английский

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A Novel Hybrid Algorithms for Groundwater Level Prediction

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2023, Номер 47(5), С. 3147 - 3164

Опубликована: Март 9, 2023

Язык: Английский

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Effects of elevated arsenic and nitrate concentrations on groundwater resources in deltaic region of Sundarban Ramsar site, Indo-Bangladesh region

Marine Pollution Bulletin, Год журнала: 2023, Номер 188, С. 114618 - 114618

Опубликована: Янв. 20, 2023

Язык: Английский

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An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Journal of Environmental Management, Год журнала: 2024, Номер 351, С. 119896 - 119896

Опубликована: Янв. 3, 2024

Язык: Английский

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Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning

The Science of The Total Environment, Год журнала: 2024, Номер 923, С. 171312 - 171312

Опубликована: Фев. 28, 2024

Язык: Английский

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Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model

Journal of Cleaner Production, Год журнала: 2022, Номер 374, С. 134011 - 134011

Опубликована: Сен. 8, 2022

Язык: Английский

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Effect of hydrogeochemical behavior on groundwater resources in Holocene aquifers of moribund Ganges Delta, India: Infusing data-driven algorithms

Environmental Pollution, Год журнала: 2022, Номер 314, С. 120203 - 120203

Опубликована: Сен. 20, 2022

Язык: Английский

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Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia

Heliyon, Год журнала: 2023, Номер 9(7), С. e17689 - e17689

Опубликована: Июнь 30, 2023

Accurate water level prediction for both lake and river is essential flood warning freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory (LSTM) extreme gradient boosting XGBoost were applied to develop forecasting models in Muda River, Malaysia. The developed using limited amount of daily meteorological data from 2016 2018. Different input scenarios tested investigate the performance models. results evaluation showed that MLP model outperformed LSTM predicting levels, with an overall accuracy score 0.871 compared 0.865 0.831 XGBoost. No noticeable improvement has been achieved after incorporating into Even though lowest reported was by XGBoost, it faster algorithms due its advanced parallel processing capabilities distributed computing architecture. terms different time horizons, found be more accurate than when 7 days ahead, demonstrating superiority capturing long-term dependencies. Therefore, can concluded each ML own merits weaknesses, differs on case because these depends largely quantity quality available training.

Язык: Английский

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