Investigating the Performance of the Informer Model for Streamflow Forecasting

Water, Год журнала: 2024, Номер 16(20), С. 2882 - 2882

Опубликована: Окт. 10, 2024

Recent studies have shown the potential of transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to series flood events using deep learning techniques is examined, with a particular focus on evaluating performance Informer model (a implementation transformer architecture), attempts address previous issues. predictive capabilities are explored compared statistical methods, stochastic models traditional networks. accuracy, efficiency well limits approaches demonstrated via numerical benchmarks relating real river streamflow applications. Using daily flow data from River Test England main case study, we conduct rigorous evaluation efficacy capturing complex temporal dependencies inherent series. analysis extended encompass diverse datasets various locations (>100) United Kingdom, providing insights into generalizability Informer. results highlight superiority over established forecasting especially regarding LSTF problem. For forecast horizon 168 days, achieves an NSE 0.8 maintains MAPE below 10%, while second-best (LSTM) only −0.63 25%, respectively. Furthermore, it observed that dependence structure series, expressed by climacogram, affects network.

Язык: Английский

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A novel hybrid model based on dual-layer decomposition and kernel density estimation for VOCs concentration forecasting considering influencing factors

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102439 - 102439

Опубликована: Фев. 1, 2025

Язык: Английский

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Improved non-dominated sorting genetic algorithm III for efficient of multi-objective cascade reservoirs scheduling under different hydrological conditions

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132998 - 132998

Опубликована: Март 1, 2025

Язык: Английский

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Ensemble Framework for Multi-scale Runoff Interval Forecasting using Weight Combination and Reconstruction Strategy

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Март 6, 2025

Язык: Английский

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Innovative Knowledge-Based System for Streamflow Hindcasting: A Comparative Assessment of Gaussian Process-Integrated Neural Network with LSTM and GRU Models

Environmental Modelling & Software, Год журнала: 2025, Номер unknown, С. 106433 - 106433

Опубликована: Март 1, 2025

Язык: Английский

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