Multimodal selective state space model-based time series classification for electricity theft detection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 278, P. 127364 - 127364

Published: April 2, 2025

Language: Английский

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Cascaded neural network surrogate modeling for real-time decision-making in long-distance water supply distribution

Engineering Applications of Computational Fluid Mechanics, Journal Year: 2025, Volume and Issue: 19(1)

Published: Jan. 16, 2025

Effective water distribution in long-distance supply systems requires precise control over pump station operations and flow-regulating elements, such as speeds valve openings, typically achieved through hydraulic models. However, traditional models are time-intensive to develop require frequent calibration, limiting their practicality for real-time applications. This paper presents a cascaded neural network (CNN) model that integrates classification regression components serve an efficient surrogate decision-making. In the proposed CNN model, component identifies number of pumps needed meet system flow demands, while predicts target values openings. Considering nonlinear relationship between rate regulating error was introduced evaluation metric via Orthogonal-Triangular (QR) decomposition. The model's performance robustness were validated using data from actual system, including analyses its sensitivity uncertainties reservoir level measurements. Results demonstrate achieves more accurate predictions compared pure networks. Furthermore, uncertainty analysis reveals is less affected by measurement errors, it sensitive underscoring importance monitoring practical

Language: Английский

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City-Scale High-Resolution Flood Nowcasting Based on High-Performance Hydrodynamic Modelling

Published: Jan. 1, 2025

Language: Английский

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Capturing Urban Pluvial River Flooding Features Based on the Fusion of Physically Based and Data-Driven Approaches

Sustainability, Journal Year: 2025, Volume and Issue: 17(6), P. 2524 - 2524

Published: March 13, 2025

Driven by climate change and rapid urbanization, pluvial flooding is increasingly endangering urban environments, prompting the widespread use of coupled hydrological–hydrodynamic models that enable more accurate flood simulations enhanced forecasting. The simulation method for river caused heavy rainfall has garnered growing attention. However, existing studies primarily concentrate on prediction using hydrodynamic or machine learning models, there remains a dearth comprehensive framework couples both to simulate temporal evolution changes. This research proposes novel simulating integrating physically based with deep approaches. sample set through data augmentation Generative Adversarial Networks, scheduling control signals are incorporated into encoder–decoder architecture results demonstrate strong model performance, provided model’s structural complexity aligned available training data. After incorporating information, simulated water level process exhibits “double-peak” pattern, where first peak noticeably lower than under non-scheduling conditions. current introduces an innovative analyzing large-scale flooding, offering valuable perspectives planning mitigation strategies.

Language: Английский

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SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133280 - 133280

Published: April 1, 2025

Language: Английский

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Urban Flood Modelling: Challenges and Opportunities - A Stakeholder-Informed Analysis

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: unknown, P. 106507 - 106507

Published: April 1, 2025

Language: Английский

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