Air temperature forecasting using artificial neural network for Ararat valley

Astsatryan, Hrachya; Grigoryan, Hayk; Poghosyan, Aghasi S.; Abrahamyan, Rita; Asmaryan, Shushanik; Muradyan, Vahagn; Tepanosyan, Garegin; Guigoz, Yaniss; Giuliani, Gaston

doi:10.1007/s12145-021-00583-9

Cited by 41 publications

(15 citation statements)

References 29 publications

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“…Compared with other machine learning methods such as neural networks (Astsatryan et al, 2021), the RF model has better noise immunity and is suitable for small sample sizes in this study. Other machine learning methods usually require a lot of data with little noise, so the data cleaning before modeling will take more time.…”

Section: Discussionmentioning

confidence: 99%

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

et al. 2022

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Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high-spatial-resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. Firstly, the observed environmental parameters, e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF), around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 ∘C. Then, the spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the decreasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high spatial and temporal resolution (30 m and 1 h) CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

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Section: Discussionmentioning

confidence: 99%

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

et al. 2022

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“…Astsatryan et al. ( 2021 ) utilized the neural network technique for the prediction of hourly temperature in the Ararat valley, Armenia. The results revealed that the suggested model provided and accuracy in the prediction of temperature for 3 and 24 h.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

Shad

Sharma

Singh

2022

Model. Earth Syst. Environ.

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Relative humidity plays an important role in climate change and global warming, making it a research area of greater concern in recent decades. The present study attempted to implement seasonal autoregressive moving average (SARIMA) and artificial neural network (ANN) with multilayer perceptron (MLP) models to forecast the monthly relative humidity in Delhi, India during 2017–2025. The average monthly relative humidity data for the period 2000–2016 have been used to carry out the objectives of the proposed study. The forecast trend in relative humidity declines from 2017 to 2025. The accuracy of the models has been measured using root mean squared error (RMSE) and mean absolute error (MAE). The results showed that the SARIMA model provides the forecasted relative humidity with RMSE of 6.04 and MAE of 4.56. On the other hand, MLP model reported the forecasted relative humidity with RMSE of 4.65 and MAE of 3.42. This study concluded that the ANN model was more reliable for predicting relative humidity than SARIMA model. Supplementary Information The online version contains supplementary material available at 10.1007/s40808-022-01385-8.

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“…Predictions are made by establishing models of various variables and AT, such as RF models or neural networks. Compared with other machine learning methods such as neural networks (Astsatryan et al, 2021), the RF model has better noise immunity and is suitable for small sample sizes in this study. Other machine learning methods usually require a lot of data with little noise, so the data cleaning before modeling will take more time.…”

Air temperature forecasting using artificial neural network for Ararat valley

Cited by 41 publications

References 29 publications

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models

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