The Science of The Total Environment, Год журнала: 2023, Номер 912, С. 169656 - 169656
Опубликована: Дек. 27, 2023
Язык: Английский
The Science of The Total Environment, Год журнала: 2023, Номер 912, С. 169656 - 169656
Опубликована: Дек. 27, 2023
Язык: Английский
Journal of Cleaner Production, Год журнала: 2022, Номер 372, С. 133675 - 133675
Опубликована: Авг. 24, 2022
Язык: Английский
Процитировано
87Hydrology, 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
Процитировано
51Composite Structures, Год журнала: 2022, Номер 306, С. 116599 - 116599
Опубликована: Дек. 15, 2022
Язык: Английский
Процитировано
63Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2023, Номер 47(5), С. 3147 - 3164
Опубликована: Март 9, 2023
Язык: Английский
Процитировано
31Marine Pollution Bulletin, Год журнала: 2023, Номер 188, С. 114618 - 114618
Опубликована: Янв. 20, 2023
Язык: Английский
Процитировано
25Journal of Environmental Management, Год журнала: 2024, Номер 351, С. 119896 - 119896
Опубликована: Янв. 3, 2024
Язык: Английский
Процитировано
15The Science of The Total Environment, Год журнала: 2024, Номер 923, С. 171312 - 171312
Опубликована: Фев. 28, 2024
Язык: Английский
Процитировано
10Journal of Cleaner Production, Год журнала: 2022, Номер 374, С. 134011 - 134011
Опубликована: Сен. 8, 2022
Язык: Английский
Процитировано
31Environmental Pollution, Год журнала: 2022, Номер 314, С. 120203 - 120203
Опубликована: Сен. 20, 2022
Язык: Английский
Процитировано
31Heliyon, 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
Процитировано
23