A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Water, Год журнала: 2024, Номер 16(19), С. 2870 - 2870

Опубликована: Окт. 9, 2024

Climate change affects the water cycle, resource management, and sustainable socio-economic development. In order to accurately predict climate in Weifang City, China, this study utilizes multiple data-driven deep learning models. The data for 73 years include monthly average air temperature (MAAT), minimum (MAMINAT), maximum (MAMAXAT), total precipitation (MP). different models artificial neural network (ANN), recurrent NN (RNN), gate unit (GRU), long short-term memory (LSTM), convolutional (CNN), hybrid CNN-GRU, CNN-LSTM, CNN-LSTM-GRU. CNN-LSTM-GRU MAAT prediction is best-performing model compared other with highest correlation coefficient (R = 0.9879) lowest root mean square error (RMSE 1.5347) absolute (MAE 1.1830). These results indicate that method a suitable model. This can also be used surface modeling. will help flood control management.

Язык: Английский

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A Hybrid Improved Dual-Channel and Dual-Attention Mechanism Model for Water Quality Prediction in Nearshore Aquaculture

Electronics, Год журнала: 2025, Номер 14(2), С. 331 - 331

Опубликована: Янв. 15, 2025

The aquatic environment in aquaculture serves as the foundation for survival and growth of animals, while a high-quality water is necessary condition promoting efficient healthy development. To effectively guide early warnings regulation quality aquaculture, this study proposes predictive model based on dual-channel dual-attention mechanism, namely, DAM-ResNet-LSTM model. This encompasses two parallel feature extraction channels: residual network (ResNet) long short-term memory (LSTM), with mechanisms integrated into each channel to enhance model’s representation capabilities. Then, proposed trained, validated, tested using meteorological parameter data collected by an offshore farm environmental monitoring system. results demonstrate that structure mechanism can significantly improve performance prediction accuracy pH, dissolved oxygen (DO), salinity (SAL) (with Nash coefficients 0.9361, 0.9396, 0.9342, respectively) higher than chemical demand (COD), ammonia nitrogen (NH3-N), nitrite (NO2−), active phosphate (AP) 0.8578, 0.8542, 0.8372, 0.8294, respectively). Compared single-channel DA-ResNet (ResNet mechanism), predicting DO, SAL, COD, NH3-N, NO2−, AP increase 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, 14.99%, respectively. DA-LSTM (LSTM corresponding increases are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, 10.2%, ResNet-LSTM LSTM parallel) without attention improvements 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, 4.13%, fulfills practical requirements accurate forecasting nearshore aquaculture.

Язык: Английский

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Comparative study of multivariate hybrid neural networks for global sea level prediction through 2050

Environmental Earth Sciences, Год журнала: 2025, Номер 84(3)

Опубликована: Янв. 21, 2025

Язык: Английский

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Enhancing temperature prediction in the UAE: a process-driven framework for adaptive learning with GRU-CNN hybrid models

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(2)

Опубликована: Янв. 22, 2025

Язык: Английский

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A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting

Water Resources Management, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 29, 2024

Язык: Английский

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Decompose-deep-recompose models and genetic algorithm based optimal ensemble method (GAE) to enhance the air temperature forecasting of world’s major urban cities

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Фев. 1, 2025

Язык: Английский

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Statistical and deep learning approaches in estimating present and future global mean sea level rise

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Март 15, 2025

Язык: Английский

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A machine learning approach for the efficient estimation of ground-level air temperature in urban areas

Urban Climate, Год журнала: 2025, Номер 61, С. 102415 - 102415

Опубликована: Апрель 24, 2025

Язык: Английский

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Time Series Analysis of Sea Surface Temperature Change in the Coastal Seas of Türkiye

Journal of Atmospheric and Solar-Terrestrial Physics, Год журнала: 2024, Номер 263, С. 106339 - 106339

Опубликована: Авг. 28, 2024

Язык: Английский

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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer

Sensors, Год журнала: 2024, Номер 24(23), С. 7837 - 7837

Опубликована: Дек. 7, 2024

Weather prediction is of great significance for human daily production activities, global extreme climate prediction, and environmental protection the Earth. However, existing data-based weather methods cannot adequately capture spatial temporal evolution characteristics target region, which makes it difficult to meet practical application requirements in terms efficiency accuracy. Changes involve both strongly correlated continuation relationships, at same time, variables interact with each other, so capturing dynamic correlations among space, particularly important accurate prediction. Therefore, we designed a spatiotemporal coupled network based on convolution Transformer from perspective multivariate fields. First, attention encoder-decoder comprehensively explore representations extracting reconstructing features. Then, multi-scale module obtain patterns using inter- intra-frame computations. After that, order ensure that model has better ability local hotspot areas, composite loss function MSE SSIM focus structural distribution achieve more Finally, demonstrated excellent effect STWPM field by evaluating proposed algorithm classical algorithms ERA5 dataset region.

Язык: Английский

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