The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction

Zahedi, Gholamreza; Afzali, Mahboubeh; Afzali, Afsaneh

doi:10.7763/ijesd.2012.v3.183

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…Experimental results showed the best performance for a configuration defined by a 5 hidden-layer network with 10 or 16 neurons and a tan-sigmoid transfer function. An alternative Elman ANN approach was proposed by Afzali et al [83] to predict mean, minimum, and maximum temperature during the years 1961-2004 in Kerman city, located in the south east of Iran. The one-day and one-month ahead air temperature is predicted slightly more precisely with this approach compared to the traditional MLPNN.…”

Section: Daily Temperature Forecastingmentioning

confidence: 99%

“…In addition, the authors implemented three optimization methods: back-propagation (BP), Genetic Algorithm (GA) and combined GA-Particle Swarm Optimization (PSO), showing a better performance in the BP results. Research developed by Afzali et al [83], described in the previous section, addressed the monthly temperature prediction as well. In this case, an ENN was proposed as a suitable solution, in comparison with the MLPNN.…”

Section: Monthly Temperature Forecastingmentioning

confidence: 99%

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Air Temperature Forecasting Using Machine Learning Techniques: A Review

et al. 2020

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Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep Learning strategies report smaller errors (Mean Square Error = 0.0017 °K) compared with traditional Artificial Neural Networks architectures, for 1 step-ahead at regional scale. At the global scale, Support Vector Machines are preferred based on their good compromise between simplicity and accuracy. In addition, the accuracy of the methods described in this work is found to be dependent on inputs combination, architecture, and learning algorithms. Finally, further research areas in temperature forecasting are outlined.

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Section: Daily Temperature Forecastingmentioning

confidence: 99%

Section: Monthly Temperature Forecastingmentioning

confidence: 99%

Air Temperature Forecasting Using Machine Learning Techniques: A Review

et al. 2020

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“…The statistical models can be categorized into two approaches: cointegration approaches which determine the relationship between non-stationary and stationary times series [ 13 ], and regression approaches which evaluate the characteristics of time series for a given temperature data [ 14 , 15 ]. However, since temperature prediction involves high nonlinearity and dimensionality, the statistical models faced some drawbacks in capturing them [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Data-driven models for atmospheric air temperature forecasting at a continental climate region

et al. 2022

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Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

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“…These techniques have the advantage in understanding the air quality status in the studied area by interpreting the complex databases, as well as benefits in the programmes for monitoring the air quality with efficient management. Nevertheless, the prediction on air quality had become a challenge when using a simple mathematical formula especially involved with complex data (Mutalib et al, 2013) which are incapable of relating the non-linear with the different variables (Afzali et al, 2012). Since the air quality exists in the stochastic time series and are able to be predicted based on the historical data (Giorgio and Piero, 1996), therefore, it is vital to help by reducing human health issues by planning and controlling strategies for proper actions.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Ann With PCR for Progressive Developing of Air Pollution Index Forecast

Hua

Yusoh

Yusof

et al. 2022

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This study circumscribes the modelling for concentration of Air Pollutant Index (API) in Selangor, Malaysia. The five monitored environmental pollutant concentrations (O3, CO, NO2, SO2, PM10) for ten years (2006 to 2015) data are used in this study to develop the prediction of API. The selected study area is located in rapid urbanised areas and surrounded by a number of industries, and is highly influenced by congested traffic. The principal component regression (PCR) for the combination of the principal component analysis together with multiple regression analysis, and artificial neural network (ANN), are used to predict the API concentration level. An additional approach using a combination method of PCR and ANN are included into the study to improve the API accuracy of prediction. The resulting prediction models are consistent with the observed value. The prediction techniques of PCR, ANN, and a combination method of R2 values are 0.931, 0.956, and 0.991 respectively. The combination method of PCR and ANN are detected to reduce the root mean square error (RMSE) of API concentration. In conclusion, different techniques were used in the combination method of API prediction which had improved and provided better accuracy rather than being dependent on the single prediction model.

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The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction

Cited by 19 publications

References 12 publications

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Data-driven models for atmospheric air temperature forecasting at a continental climate region

Incorporating Ann With PCR for Progressive Developing of Air Pollution Index Forecast

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