An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 129977 - 129977

Published: July 22, 2023

Language: Английский

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A deep learning interpretable model for river dissolved oxygen multi-step and interval prediction based on multi-source data fusion

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 629, P. 130637 - 130637

Published: Jan. 14, 2024

Language: Английский

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Marine waters assessment using improved water quality model incorporating machine learning approaches

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118368 - 118368

Published: June 24, 2023

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends a number of factors, one the most important is quality water. The index (WQI) model widely used to assess quality, but existing models have uncertainty issues. To address this, authors introduced two new WQI models: weight based weighted quadratic mean (WQM) unweighted root squared (RMS) models. These were in Bay Bengal, using seven indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), molybdate reactive phosphorus (MRP). Both ranked between "fair" categories, with no significant difference models' results. showed considerable variation computed scores, ranging from 68 88 an average 75 for WQM 70 76 72 RMS. did not any issues sub-index or aggregation functions, had high level sensitivity (R2 = 1) terms spatio-temporal resolution waterbodies. study demonstrated that approaches effectively assessed waters, reducing improving accuracy score.

Language: Английский

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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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Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175407 - 175407

Published: Aug. 9, 2024

Language: Английский

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Artificial Intelligence in Environmental and Climate Changes

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 485 - 506

Published: Aug. 27, 2024

Artificial Intelligence plays a pivotal in resolving climate change and the environmental crisis with help of AI technologies. However, by scrubbing massive amounts information from satellites sensors, it can refine prediction allowing for better re-prioritization downstream when initiating mitigation plans. In addition, using Intelligence, also optimizes trees autonomous networks energy systems, emissions reduction carbon. But training is intensive, responsible sizable chunk greenhouse gas emissions. As evolves, essential to guide its development deployment principles sustainability responsibility. This chapter examines various aspects issues achieve sustainable goals. It significant challenges limitations intertwined incorporation degradation crises change.

Language: Английский

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Estimating the hydrogen adsorption in depleted shale gas reservoirs for kerogens in underground hydrogen storage using machine learning algorithms

Fuel, Journal Year: 2025, Volume and Issue: 388, P. 134534 - 134534

Published: Feb. 5, 2025

Language: Английский

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Aerobic Stress Detection in Aquatic Environments with Water Quality Data Using Hybrid Deep Learning Based ConvRec Model

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 3, 2025

Depletion of dissolved oxygen in the water is a serious threat to fish and other aquatic organisms, it causes aerobic stress disease fish. Detection crucial maintain better growth spawning fishes. Recently many studies proposed deep learning-based quality analysis techniques, but these techniques inadequate handling complex data. Because has both spatial temporal characteristics, this makes most learning models inadequate. To handle such multifaceted data we ConvRec, architecture that incorporates CNN (Convolution neural network) LSTM (Long-short term structures. component extracts feature domain from different locations while captures features hence model can learn correlations between movement parameters classify aqua ponds. In work use two dataset are unlabelled collected using IoT (Internet things) devices. ConvRec model, usus fine-grained annotation points have effect empowering detect relevant traits associated with It be therefore ascertained yields high degrees accuracy 99.2% 99.65%, on “ponds” “waterx” datasets respectively past only 98.2% 98.1% same datasets. These results demonstrate not promising for estimating health during deficiency also take part reducing negative impact low levels

Language: Английский

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A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast

Resources Policy, Journal Year: 2023, Volume and Issue: 83, P. 103602 - 103602

Published: April 26, 2023

Language: Английский

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A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 47, P. 101435 - 101435

Published: May 30, 2023

Tai Lake, the third largest freshwater lake in China, with a history of serious ecological pollution incidents. Lake water quality prediction techniques are essential to ensure an early emergency response capability for sustainable management. Herein, effective data-driven ensemble model was developed predicting dissolved oxygen (DO) based on meteorological factors, indicators and spatial information. First, variation mode decomposition (VMD) used decompose data into multiple modal components classify them feature terms self terms. The were combined relevant external features multivariate by convolutional neural network (CNN) bi-directional long short-term memory (BiLSTM) attention mechanism (AT), as well using whale optimization algorithm (WOA) optimize hyperparameters. form secondary model. Finally, groupings linearly summed obtain outcome. proposed has highest accuracy best effect 0.5 days period. This research also establishes stepwise temperature regulation mechanism, where output target DO content value is achieved changing magnitude combining it this model, thereby strengthening protection resources management fishery production.

Language: Английский

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