scholarly journals Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

2017 ◽  
Vol 24 (3) ◽  
pp. 457-465
Author(s):  
Eyup Selim Koksal ◽  
Bilal Cemek ◽  
Sakine Cetin ◽  
Prasanna H. Gowda ◽  
Terry A. Howell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Balance ◽  
Climatic Data ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network Approach for Estimating Reference Evapotranspir

2016 ◽  
Vol 11 (2) ◽  
pp. 637-647 ◽  
Author(s):  
Khyati Vyas ◽  
R Subbaiah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Resources ◽  
Water Cycle ◽  
Moisture Stress ◽  
Accurate Estimation ◽  
Climatic Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Artificial Neural

The process of evapotranspiration (ET) is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs) have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN) for estimating ET0 with limited climatic data set.

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.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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