Framework for multivariate carbon price forecasting: A novel hybrid model

Journal of Environmental Management, Год журнала: 2024, Номер 369, С. 122275 - 122275

Опубликована: Авг. 31, 2024

Язык: Английский

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Deep dive into predictive excellence: Transformer's impact on groundwater level prediction

Journal of Hydrology, Год журнала: 2024, Номер 636, С. 131250 - 131250

Опубликована: Апрель 28, 2024

Язык: Английский

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Unlocking the Potential of Artificial Intelligence for Sustainable Water Management Focusing Operational Applications

Water, Год журнала: 2024, Номер 16(22), С. 3328 - 3328

Опубликована: Ноя. 19, 2024

Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes their management is crucial to perform efficient resource (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, taking controlling measures manage risks ensure sustainability. Artificial intelligence (AI) techniques leverage these knowledge fields single theme. This review article focuses on the potential AI in two specific areas: supply-side demand-side measures. It includes investigation applications leak detection infrastructure maintenance, demand forecasting supply optimization, treatment desalination, quality pollution control, parameter calibration optimization applications, flood drought predictions, decision support systems. Finally, an overview selection appropriate suggested. The nature adoption investigated using Gartner hype cycle curve indicated that learning application has advanced different stages maturity, big data reach plateau productivity. also delineates pathways expedite integration AI-driven solutions harness transformative capabilities for protection global resources.

Язык: Английский

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Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Сен. 25, 2024

Язык: Английский

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Enhancing Long-Term Flood Forecasting with SageFormer: A Cascaded Dimensionality Reduction Approach Based on Satellite-Derived Data

Remote Sensing, Год журнала: 2025, Номер 17(3), С. 365 - 365

Опубликована: Янв. 22, 2025

Floods, increasingly exacerbated by climate change, are among the most destructive natural disasters globally, necessitating advancements in long-term forecasting to improve risk management. Traditional models struggle with complex dependencies of hydroclimatic variables and environmental conditions, thus limiting their reliability. This study introduces a novel framework for enhancing flood accuracy integrating geo-spatiotemporal analyses, cascading dimensionality reduction, SageFormer-based multi-step-ahead predictions. The efficiently processes satellite-derived data, addressing curse focusing on critical long-range spatiotemporal dependencies. SageFormer captures inter- intra-dependencies within compressed feature space, making it particularly effective forecasting. Performance evaluations against LSTM, Transformer, Informer across three data fusion scenarios reveal substantial improvements accuracy, especially data-scarce basins. integration hydroclimate attention-based networks reduction demonstrates significant over traditional approaches. proposed combines advanced deep learning, both interpretability precision capturing By offering straightforward reliable approach, this advances remote sensing applications hydrological modeling, providing robust tool mitigating impacts extremes.

Язык: Английский

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AI-driven weather downscaling for smart agriculture using autoencoders and transformers

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 232, С. 110129 - 110129

Опубликована: Фев. 17, 2025

Язык: Английский

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Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133097 - 133097

Опубликована: Март 1, 2025

Язык: Английский

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A data-driven LSTM-based management and control approach for fatigue life of subsea wellhead system

Ocean Engineering, Год журнала: 2024, Номер 313, С. 119335 - 119335

Опубликована: Сен. 28, 2024

Язык: Английский

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Dynamic optimal control of coal tar chemical looping gasification based on process modelling and intelligent screening

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141266 - 141266

Опубликована: Фев. 13, 2024

Язык: Английский

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A novel operational water quality mobile prediction system with LSTM-Seq2Seq model

Environmental Modelling & Software, Год журнала: 2024, Номер 185, С. 106290 - 106290

Опубликована: Дек. 9, 2024

Язык: Английский

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