Forecasting Reference Evapotranspiration Using Artificial Neural Network for Nagpur Region

2021 ◽  
pp. 67-77
Author(s):  
Nikhil Band ◽  
Aniruddha Ghare ◽  
Avinash Vasudeo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reference Evapotranspiration ◽  
Artificial Neural

scholarly journals Prediction of reference evapotranspiration using artificial neural network

2015 ◽  
Vol 8 (1) ◽  
pp. 1-8 ◽  
Author(s):  
R.V. MESHRAM ◽  
M.M. DESHMUKH ◽  
S.B. WADATKAR ◽  
M.U. KALE ◽  
A.N. MANKAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reference Evapotranspiration ◽  
Artificial Neural

Artificial Neural Network to Forecast Reference Evapotranspiration Over Grass Crop: Performance Under Missing Data

2014 ◽  
Author(s):  
E.C. Nascimento ◽  
T.R.S. Costa ◽  
R.P. Lima ◽  
L.O. Brasileiro ◽  
M.A. Borba ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Missing Data ◽  
Reference Evapotranspiration ◽  
Crop Performance ◽  
Artificial Neural

scholarly journals Modeling of Evapotranspiration (ETo) in a Medium Urban Park within a Megacity by Using Artificial Neural Network (ANN) Model

2021 ◽  
Author(s):  
Hayder Algretawee ◽  
Ghofran Alshama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrological Cycle ◽  
Reference Evapotranspiration ◽  
Coefficient Of Determination ◽  
Medium Size ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Main Component

Evapotranspiration (ETo) is considered a main component of the hydrological cycle. This study was carried out on a medium-size park within a highly urbanized area, close to the center of Melbourne city. The purpose of the study is to calculate the reference evapotranspiration (ETo), particularly at a specified spot in a corner of the park. The hand-held device used to collect data gave consistent results and reduced the need for assumptions. The Penman-Montieth equation was used to calculate the reserved ETo. To build an ETo model, Artificial Neural Network (ANN) was adopted to predict ETo. Three models were built to select the best model, based on the least Root Mean Square Error (RMSE) and the highest coefficient of determination (R2). Results showed a contrast between the observed and predicted magnitudes of ETo. Both of the observed and predicted magnitudes for ETo are higher than most recent studies. Data from the specified location shows a difference in ETo magnitudes relative to the fixed meteorological stations. This study supports that climate change causes increasing magnitudes of reference evapotranspiration ETo.

scholarly journals Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Armin Alipour ◽  
Jalal Yarahmadi ◽  
Maryam Mahdavi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrological Cycle ◽  
Reference Evapotranspiration ◽  
Weather Data ◽  
Climatic Data ◽  
M5 Model Tree ◽  
Model Tree ◽  
Artificial Neural ◽  
M5 Model

Reference evapotranspiration (ETO) is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determine ETO using land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimate ETO values and their results were compared with calculated ETO by FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculate ETO. LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination (R2) values for comparison between calculated ETO by FAO-PM and estimated ETO by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, R2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ETO by means of LST data derived from MODIS sensor.

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