Analyzing surface air temperature and rainfall in univariate framework, quantifying uncertainty through Shannon entropy and prediction through artificial neural network

2021 ◽  
Author(s):  
Samayita Nag Ray ◽  
Surajit Chattopadhyay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Shannon Entropy ◽  
Surface Air Temperature ◽  
Artificial Neural ◽  
Quantifying Uncertainty

scholarly journals Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

2017 ◽  
Vol 24 (4) ◽  
pp. 603-611 ◽  
Author(s):  
Alireza Araghi ◽  
Mohammad Mousavi-Baygi ◽  
Jan Adamowski ◽  
Christopher Martinez ◽  
Martine van der Ploeg
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Artificial Neural

scholarly journals A GA-Based BP Artificial Neural Network for Estimating Monthly Surface Air Temperature of the Antarctic during 1960–2019

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Miao Fang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Antarctic Continent ◽  
Cooling Trend ◽  
Artificial Neural ◽  
June July ◽  
The Antarctic

The spatial sparsity and temporal discontinuity of station-based SAT data do not allow to fully understand Antarctic surface air temperature (SAT) variations over the last decades. Generating spatiotemporally continuous SAT fields using spatial interpolation represents an approach to address this problem. This study proposed a backpropagation artificial neural network (BPANN) optimized by a genetic algorithm (GA) to estimate the monthly SAT fields of the Antarctic continent for the period 1960–2019. Cross-validations demonstrate that the interpolation accuracy of GA-BPANN is higher than that of two benchmark methods, i.e., BPANN and multiple linear regression (MLR). The errors of the three interpolation methods feature month-dependent variations and tend to be lower (larger) in warm (cold) months. Moreover, the annual SAT had a significant cooling trend during 1960–1989 (trend = −0.07°C/year; p = 0.04 ) and a significant warming trend during 1990–2019 (trend = 0.06°C/year; p = 0.05 ). The monthly SAT did not show consistent cooling or warming trends in all months, e.g., SAT did not show a significant cooling trend in January and December during 1960–1989 and a significant warming trend in January, June, July, and December during 1990–2019. Furthermore, the Antarctic SAT decreases with latitude and the distance away from the coastline, but the eastern Antarctic is overall colder than the western Antarctic. Spatiotemporal inconsistencies on SAT trends are apparent over the Antarctic continent, e.g., most of the Antarctic continent showed a cooling trend during 1960–1989 (trend = −0.20∼0°C/year; p = 0.01 ∼ 0.27 ) with a peak over the central part of the eastern Antarctic continent, while the entire Antarctic continent showed a warming trend during 1990–2019 (trend = 0∼0.10°C/year; p = 0.04 ∼ 0.42 ) with a peak over the higher latitudes.

scholarly journals Estimation of microclimatic data in remote mountainous areas using an artificial neural network model-based approach

2013 ◽  
Vol 12 (4) ◽  
pp. 384-389
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Application Site ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Model Based ◽  
Artificial Neural

An artificial neural network (ANN) model-based approach was developed and applied to estimate values of air temperature and relative humidity in remote mountainous areas. The application site was the mountainous area of the Samaria National Forest canyon (Greece). Seven meteorological stations were established in the area and ANNs were developed to predict air temperature and relative humidity for the five most remote stations of the area using data only from two stations located in the two more easily accessed sites. Measured and model-estimated data were compared in terms of the determination coefficient (R2), the mean absolute error (MAE) and residuals normality. Results showed that R2 values range from 0.7 to 0.9 for air temperature and from 0.7 to 0.8 for relative humidity whereas MAE values range from 0.9 to 1.8 oC and 5 to 9%, for air temperature and relative humidity, respectively. In conclusion, the study demonstrated that ANNs, when adequately trained, could have a high applicability in estimating meteorological data values in remote mountainous areas with sparse network of meteorological stations, based on a series of relatively limited number of data values from nearby and easily accessed meteorological stations.

scholarly journals Air Temperature Estimation by Using Artificial Neural Network Models in the Greater Athens Area, Greece

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
A. P. Kamoutsis ◽  
A. S. Matsoukis ◽  
K. I. Chronopoulos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Input Parameter ◽  
Network Models ◽  
Reference Station ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Network Approaches ◽  
Athens Area

Air temperature (T) data were estimated in the regions of Nea Smirni, Penteli, and Peristeri, in the greater Athens area, Greece, using the T data of a reference station in Penteli. Two artificial neural network approaches were developed. The first approach, MLP1, used the T as input parameter and the second, MLP2, used additionally the time of the corresponding T. One site in Nea Smirni, three sites in Penteli, from which two are located in the Pentelikon mountain, and one site in Peristeri were selected based on different land use and altitude. T data were monitored in each site for the period between December 1, 2009, and November 30, 2010. In this work the two extreme seasons (winter and summer) are presented. The results showed that the MLP2 model was better (higher and lower MAE) than MLP1 for the T estimation in both winter and summer, independently of the examined region. In general, MLP1 and MLP2 models provided more accurate T estimations in regions located in greater distance (Nea Smirni and Peristeri) from the reference station in relation to the nearby Pentelikon mountain. The greater distance T estimations, in most cases, were better in winter compared to summer.

scholarly journals Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Ali Najah Ahmed ◽  
Nur’atiah Zaini ◽  
Arif Razzaq ◽  
Pavitra Kumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Unseen Data ◽  
Temperature And Humidity ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Neural Models to Predict Temperature and Natural Ventilation in a High Tunnel

2019 ◽  
Vol 62 (3) ◽  
pp. 761-769 ◽  
Author(s):  
Muzi Zheng ◽  
Brian Leib ◽  
Wesley Wright ◽  
Paul Ayers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Air Temperature ◽  
Crop Protection ◽  
Ventilation Rate ◽  
Energy Utilization ◽  
High Tunnel ◽  
Growth Environment ◽  
Door Opening ◽  
Artificial Neural

Abstract. As a response to the rising demand for local food, high tunnels (HTs) can help small producers become more profitable through crop protection and extension of the growing season. Proper ventilation that responds to changes in outside weather conditions can remove excess heat and humidity inside HTs and lead to better solar energy utilization while maintaining a favorable growth environment. Rather than depending on complex mathematical models, this study investigated an artificial neural network (ANN) for predicting the inside air temperature and ventilation rate of a HT. Energy balance calculations and measured values were compared to the ANN. Results showed that the average air temperature from an array of 15 thermistors inside the HT was predicted more accurately in terms of mean square error (MSE = 1.7°C2) and mean absolute error (MAE = 1.0°C) than a single inside temperature at the center of the HT (MSE = 17.7°C2, MAE = 3.3°C). Relative humidity and wind direction had the least significant impacts on the prediction of inside air temperature, and only four outside weather inputs were found to have significant impacts on the prediction of inside temperature: outside air temperature, door opening level, solar radiation, and wind speed. Moreover, the optimal ANN structure was determined as 29, 25, and 13 neurons in a single hidden layer and 30 neurons in two hidden layers for prediction of inside air temperature, ventilation rate based on measurement, door opening level, and ventilation rate based on modeling, respectively. Keywords: Air temperature control, Artificial neural network, High tunnel, Ventilation.

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