Monthly climate prediction using deep convolutional neural network and long short-term memory

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 31, 2024

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include neural network (ANN), recurrent NN (RNN), long short-term memory (LSTM), deep convolutional (CNN), CNN-LSTM. These used forecast six climatic factors on a monthly ahead. data for 72 years (1 January 1951–31 December 2022) this study average atmospheric temperature, extreme minimum maximum precipitation, relative humidity, sunlight hours. time series 12 month delayed as input signals the models. efficiency examined utilizing diverse evaluation criteria namely mean absolute error, root square error (RMSE), correlation coefficient (R). modeling result inherits that hybrid CNN-LSTM model achieves greater accuracy than other compared significantly reduces forecasting one step For instance, RMSE values ANN, RNN, LSTM, CNN, temperature stage 2.0669, 1.4416, 1.3482, 0.8015 0.6292 °C, respectively. findings simulations shows potential improve forecasting. prediction will contribute meteorological disaster prevention reduction, well flood control drought resistance.

Язык: Английский

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The Characteristics of Air Quality Changes in Hohhot City in China and their Relationship with Meteorological and Socio-economic Factors

Aerosol and Air Quality Research, Год журнала: 2024, Номер 24(5), С. 230274 - 230274

Опубликована: Янв. 1, 2024

Air pollution affects sustainable development of the natural environment and social economy. In this article, changes in air quality index (AQI) six pollutants Hohhot during 2014–2022 are analyzed. The results imply that annual average concentrations five (SO2, PM10, PM2.5, NO2, CO) AQI values declined year by over 2014–2022. Compared with 2014, fell 22.5% 2022. However, O3 increased year. PM2.5 PM10 were major factors influencing AQI. Among types atmospheric pollutants, relationship between NO2 is strongest, implying plays a significant influence formation PM2.5. Meteorological socio-economic have impact on quality. wind speed (AWS), pressure (AP), sulfur dioxide emissions (SOE), nitrogen oxide (NOE), particulate matter (PME) positive effect provide information great importance for management City.

Язык: Английский

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Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China

Atmosphere, Год журнала: 2024, Номер 15(12), С. 1432 - 1432

Опубликована: Ноя. 28, 2024

Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, data in Dezhou City China are collected from January 2014 to December 2023, multiple deep learning models used forecast PM2.5 concentrations. The ability of the is evaluated compared with observed using various statistical parameters. Although all eight can accomplish forecasting assignments, precision accuracy CNN-GRU-LSTM method 34.28% higher than that ANN method. result shows has best performance other seven models, achieving an R (correlation coefficient) 0.9686 RMSE (root mean square error) 4.6491 μg/m3. values CNN, GRU LSTM 57.00%, 35.98% 32.78% method, respectively. results reveal predictor remarkably improves performances benchmark overall forecasting. This research provides a new perspective for predictive ambient model provide scientific basis prevention control.

Язык: Английский

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Assessing the effectiveness of long short-term memory and artificial neural network in predicting daily ozone concentrations in Liaocheng City

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 25, 2025

Ozone pollution affects food production, human health, and the lives of individuals. Due to rapid industrialization urbanization, Liaocheng has experienced increasing ozone concentration over several years. Therefore, become a major environmental problem in City. Long short-term memory (LSTM) artificial neural network (ANN) models are established predict concentrations City from 2014 2023. The results show general improvement accuracy LSTM model compared ANN model. Compared ANN, an increase determination coefficient (R2), value 0.6779 0.6939, decrease root mean square error (RMSE) 27.9895 μg/m3 27.2140 absolute (MAE) 21.6919 20.8825 μg/m3. prediction is superior terms R, RMSE, MAE. In summary, promising technique for predicting concentrations. Moreover, by leveraging historical data enables accurate predictions future on global scale. This will open up new avenues controlling mitigating pollution.

Язык: Английский

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Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Frontiers in Forests and Global Change, Год журнала: 2023, Номер 6

Опубликована: Дек. 8, 2023

Introduction Atmospheric temperature affects the growth and development of plants has an important impact on sustainable forest ecological systems. Predicting atmospheric is crucial for management planning. Methods Artificial neural network (ANN) deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional (CNN), CNN-GRU, CNN-LSTM, were utilized to predict change monthly average extreme temperatures in Zhengzhou City. Average data from 1951 2022 divided into training sets (1951–2000) prediction (2001–2022), 22 months used model input next month. Results Discussion The number neurons hidden layer was 14. Six different algorithms, along with 13 various functions, trained compared. ANN evaluated terms correlation coefficient (R), root mean square error (RMSE), absolute (MAE), good results obtained. Bayesian regularization (trainbr) best performing algorithm predicting average, minimum maximum compared other algorithms R (0.9952, 0.9899, 0.9721), showed lowest values RMSE (0.9432, 1.4034, 2.0505), MAE (0.7204, 1.0787, 1.6224). CNN-LSTM performance. This method had generalization ability could be forecast areas. Future climate changes projected using model. temperature, 2030 predicted 17.23 °C, −5.06 42.44 whereas those 2040 17.36 −3.74 42.68 respectively. These suggest that continue warming future.

Язык: Английский

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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 spatiotemporal CNN-LSTM deep learning model for predicting soil temperature in diverse large-scale regional climates

The Science of The Total Environment, Год журнала: 2025, Номер 968, С. 178901 - 178901

Опубликована: Фев. 22, 2025

Язык: Английский

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Prediction of school PM2.5 by an attention-based deep learning approach informed with data from nearby air quality monitoring stations

Chemosphere, Год журнала: 2025, Номер 375, С. 144241 - 144241

Опубликована: Фев. 24, 2025

Язык: Английский

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Integrating machine learning techniques for Air Quality Index forecasting and insights from pollutant-meteorological dynamics in sustainable urban environments

Earth Science Informatics, Год журнала: 2024, Номер 17(4), С. 3733 - 3748

Опубликована: Июнь 21, 2024

Язык: Английский

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Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models

Environmental Science and Pollution Research, Год журнала: 2023, Номер 31(2), С. 2167 - 2186

Опубликована: Дек. 6, 2023

Язык: Английский

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