Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 178, P. 106091 - 106091

Published: May 28, 2024

Language: Английский

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Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Water Resources Research, Journal Year: 2023, Volume and Issue: 59(9)

Published: Sept. 1, 2023

Abstract Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas, previous research has primarily focused on historical and precipitation variability while disregarding other factors' influence. Additionally, the prediction process of most machine learning models is opaque, resulting low interpretability model predictions. Hence, this study develops an ensemble deep to forecast from three hydrological stations. Initially, time‐varying filtered based empirical mode decomposition employed decompose series into several internal functions (IMFs). Subsequently, complexity each IMF component evaluated by multi‐scale permutation entropy, IMFs are classified high‐ low‐frequency portions entropy values. Considering high‐frequency still exhibit great volatility, robust local mean adopted perform secondary portions. Then, meteorological variables processed Relief algorithm variance inflation factor features as inputs, individual subsequences preliminary outputs bidirectional gated recurrent unit extreme models. Random forests (RF) introduced nonlinear predicted sub‐models obtain final results. The proposed outperforms various evaluation metrics. Meanwhile, due opaque nature models, shapley assess contribution selected variable long‐term trend runoff. could serve essential reference for precise warning.

Language: Английский

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Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105508 - 105508

Published: May 5, 2024

Language: Английский

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Research progress of inland river water quality monitoring technology based on unmanned aerial vehicle hyperspectral imaging technology

Environmental Research, Journal Year: 2024, Volume and Issue: 257, P. 119254 - 119254

Published: May 28, 2024

Language: Английский

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A novel interval decomposition correlation particle swarm optimization-extreme learning machine model for short-term and long-term water quality prediction

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130034 - 130034

Published: Aug. 11, 2023

Language: Английский

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Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 169253 - 169253

Published: Dec. 14, 2023

Language: Английский

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Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

Environmental Research, Journal Year: 2024, Volume and Issue: 248, P. 118267 - 118267

Published: Jan. 18, 2024

Language: Английский

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A novel hierarchical approach to insight to spectral characteristics in surface water of karst wetlands and estimate its non-optically active parameters using field hyperspectral data

Water Research, Journal Year: 2024, Volume and Issue: 257, P. 121673 - 121673

Published: April 24, 2024

Language: Английский

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Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks

Water Research, Journal Year: 2024, Volume and Issue: 263, P. 122160 - 122160

Published: July 27, 2024

Language: Английский

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A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Sustainability, Journal Year: 2023, Volume and Issue: 15(14), P. 11275 - 11275

Published: July 19, 2023

Reliable and precise estimation of solar energy as one the green, clean, renewable inexhaustible types energies can play a vital role in management, especially developing countries. Also, has less impact on earth’s atmosphere environment help to lessen negative effects climate change by lowering level emissions greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel (KELM), online sequential (OSELM), optimally pruned (OPELM), outlier robust (ORELM), deep (DELM), their versions combined with variational mode decomposition (VMD) integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, VMD-ELM), for radiation Kurdistan region, Iraq. The daily meteorological data from 2017 2018 were used implement suggested at Darbandikhan Dukan stations, input parameters included maximum temperature (MAXTEMP), minimum (MINTEMP), relative humidity (MAXRH), (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), cloud cover (CLOUDCOV). results show that proposed VMD-DELM algorithm considerably enhanced simulation accuracy standalone models’ prediction, average improvement terms RMSE 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, 48.25% station 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, 40.33% station, respectively. outcomes this reveal two-stage model performed superiorly other approaches predicting considering climatic predictors both stations.

Language: Английский

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