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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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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Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models

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

Опубликована: Янв. 2, 2025

Язык: Английский

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Change in Air Quality during 2014–2021 in Jinan City in China and Its Influencing Factors

Toxics, Год журнала: 2023, Номер 11(3), С. 210 - 210

Опубликована: Фев. 24, 2023

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) concentrations of six pollutants Jinan during 2014–2021. The results indicate that annual average PM10, PM2.5, NO2, SO2, CO, O3 AQI values all declined year after Compared with 2014, City fell by 27.3% 2021. four seasons 2021 was obviously better than 2014. PM2.5 concentration highest winter lowest summer, while it opposite for concentration. COVID epoch 2020 remarkably lower compared same Nevertheless, post-COVID conspicuously deteriorated Socioeconomic elements were main reasons quality. majorly influenced energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx (NOE), particulate (PE), PM10. Clean policies played a key role improving Unfavorable meteorological conditions led to heavy weather winter. These could provide scientific reference control City.

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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PM_2.5 Concentration Prediction Using CNN‐LSTM Model Based on Multi‐Feature Fusion

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(4-5)

Опубликована: Фев. 11, 2025

ABSTRACT In order to solve the problem that existing PM 2.5 concentration prediction methods ignore spatial and temporal influencing factors of concentration, this paper constructs a characteristic factor based on maximum information coefficient, proposes CNN‐LSTM combined model multi‐feature fusion, which transforms abstract into quantifiable features. The has good feature extraction ability strong capture short‐term transient long‐range dependent in time series data, improves performance model. experimental results show accuracy fusion is 87.21%, MAPE 6.25, 4.84, 1.29 less than BP, SVR, LightGBM, 1.91 7.04 CNN LSTM.

Язык: Английский

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A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City

Toxics, Год журнала: 2025, Номер 13(4), С. 254 - 254

Опубликована: Март 28, 2025

Surface air pollution affects ecosystems and people’s health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), gated recurrent unit (BiGRU). The data meteorological factors pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs the models. W-CNN-BiGRU-BiLSTM demonstrated strong performance during phase, achieving an R (correlation coefficient) of 0.9952, root mean square error (RMSE) 1.4935 μg/m3, absolute (MAE) 1.2091 percentage (MAPE) 7.3782%. Correspondingly, accurate is beneficial control urban planning.

Язык: Английский

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A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST

Environmental Monitoring and Assessment, Год журнала: 2023, Номер 195(12)

Опубликована: Ноя. 11, 2023

Язык: Английский

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