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 Short-term forecasting of soil temperature using artificial neural network

2014 ◽  
Vol 22 (3) ◽  
pp. 576-585 ◽  
Author(s):  
Hossein Tabari ◽  
P. Hosseinzadeh Talaee ◽  
Patrick Willems
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

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.

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