scholarly journals Use of an artificial neural network to predict air temperature, surface temperature, dew point and wind speed for the prediction of frost

2003 ◽  
Author(s):  
Bradley Ray Temeyer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Surface Temperature ◽  
Air Temperature ◽  
Dew Point ◽  
Artificial Neural ◽  
Temperature Surface

scholarly journals A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3350 ◽  
Author(s):  
Kittipong Kasantikul ◽  
Dongkai Yang ◽  
Qiang Wang ◽  
Aung Lwin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Particle Filter ◽  
Maritime Transportation ◽  
Global Navigation Satellite Systems ◽  
Speed Estimation ◽  
Estimation Model ◽  
Main Concept ◽  
Artificial Neural

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

Estimation of average wind speed in the city of Tianguá using Artificial Neural Network

2021 ◽  
Author(s):  
Arilson F. G. Ferreira ◽  
Anderson P. de Aragao ◽  
Necio de L. Veras ◽  
Ricardo A. L. Rabelo ◽  
Petar Solic
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Average Wind Speed ◽  
Average Wind ◽  
Artificial Neural ◽  
The City

Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques

2019 ◽  
Vol 20 (3) ◽  
pp. 800-808
Author(s):  
G. T. Patle ◽  
M. Chettri ◽  
D. Jhajharia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Relative Humidity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Accurate Estimation ◽  
Pan Evaporation ◽  
Ann Models ◽  
Artificial Neural

Abstract Accurate estimation of evaporation from agricultural fields and water bodies is needed for the efficient utilisation and management of water resources at the watershed and regional scale. In this study, multiple linear regression (MLR) and artificial neural network (ANN) techniques are used for the estimation of monthly pan evaporation. The modelling approach includes the various combination of six measured climate parameters consisting of maximum and minimum air temperature, maximum and minimum relative humidity, sunshine hours and wind speed of two stations, namely Gangtok in Sikkim and Imphal in the Manipur states of the northeast hill region of India. Average monthly evaporation varies from 0.62 to 2.68 mm/day for Gangtok, whereas it varies from 1.4 to 4.3 mm/day for Imphal during January and June, respectively. Performance of the developed MLR and ANN models was compared using statistical indices such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) with measured pan evaporation values. Correlation analysis revealed that temperature, wind speed and sunshine hour had positive correlation, whereas relative humidity had a negative correlation with pan evaporation. Results showed a slightly better performance of the ANN models over the MLR models for the prediction of monthly pan evaporation in the study area.

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