scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

scholarly journals Modeling the contributions of global air temperature, synoptic-scale phenomena and soil moisture to near-surface static energy variability using artificial neural networks

2017 ◽  
Author(s):  
Sara C. Pryor ◽  
Ryan C. Sullivan ◽  
Justin T. Schoof
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Moisture ◽  
Air Temperature ◽  
Energy Content ◽  
Daily Maximum ◽  
Synoptic Scale ◽  
Near Surface ◽  
Artificial Neural ◽  
Static Energy

Abstract. The static energy content of the atmosphere is increasing at the global scale, but exhibits important sub-global and sub-regional scales of variability and is a useful parameter for integrating the net effect of changes in the partitioning of energy at the surface and for improving understanding of the causes of so-called warming-holes (i.e. locations with decreasing daily maximum air temperatures (T) or increasing trends of lower magnitude than the global mean). Further, measures of the static energy content (herein the equivalent potential temperature, θe) are more strongly linked to excess human mortality and morbidity than air temperature alone, and have great relevance in understanding causes of past heat-related excess mortality and making projections of possible future events that are likely to be associated with negative human health and economic consequences. A new non-linear statistical model for summertime daily maximum and minimum θe is developed and used to advance understanding of drivers of historical change and variability over the eastern USA. It is shown that soil moisture (SM) is particularly important in determining the magnitude of θe over regions that have previously been identified as exhibiting warming holes confirming the key importance of SM in dictating the partitioning of net radiation into sensible and latent heat and dictating trends in near-surface T and θe. Consistent with our a priori expectations, models built using Artificial Neural Networks (ANN) out-perform linear models that do not permit interaction of the predictor variables (global T, synoptic-scale meteorological conditions and SM). This is particularly marked in regions with high variability in min- and max-θe, where more complex models built using ANN with multiple hidden layers are better able to capture the day-to-day variability in θe and the occurrence of extreme max-θe. Over the entire domain the ANN with 3 hidden layers exhibits high accuracy in predicting max-θe > 347 K. The median hit rate for max-θe > 347 K is > 0.60, while the median false alarm rate ≈ 0.08.

Winter surface air temperature prediction over Japan using artificial neural networks

2021 ◽  
Author(s):  
J. V. Ratnam ◽  
Masami Nonaka ◽  
Swadhin K. Behera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Anomaly Correlation ◽  
The North ◽  
Skill Scores ◽  
Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

Artificial neural networks in the selection of shoe lasts for people with mild diabetes

2019 ◽  
Vol 64 ◽  
pp. 37-45 ◽  
Author(s):  
Chung-Chuan Wang ◽  
Ching-Hu Yang ◽  
Chung-Shing Wang ◽  
Dandan Xu ◽  
Bo-Shin Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mild Diabetes ◽  
Artificial Neural ◽  
Selection Of

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

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