Record Details

<p>Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Neural Network based Predictors for Evaporation Estimation at Jabalpur in Central India</p>
 
Added Entry - Uncontrolled Name Sivastava, Ajay K; Jawaharlal Nehru Krishi Vishwavidyalaya, Tikamgarh 472 001, Madhya Pradesh, India
Naidu, Diwakar ; Indira Gandhi Krishi Vishwavidyalaya, Raipur 492 012, Chhattisgarh, India
Bhan, Manish ; Jawaharlal Nehru Krishi Vishwavidyalaya, Jabalpur 482 004, Madhya Pradesh, India
Bal, Lalit Mohan; Jawaharlal Nehru Krishi Vishwavidyalaya, Tikamgarh 472 001, Madhya Pradesh, India
 
Uncontrolled Index Term Empirical methods, Linear regression, Machine learning, RMSE, Weather parameters
 
Summary, etc. <p>Free water evaporation is an imperative parameter for estimation of crop water requirement, and irrigation scheduling. This study aims to evaluate different techniques to estimate evaporation with weather parameters inputs. Multilayer Perception (MLR), Radial Basis Function (RBF) based neural network, traditional statistical Linear Regression (LR) approach and conventional empirical methods of Linacre and Christianson were used to estimate the evaporation at Jabalpur station situated under Kymore Plateau and Satpura Hills Agro-climatic Zone of Madhya Pradesh in the Central India. The weather parameters considered for estimation of evaporation are temperature, humidity, sunshine hours and wind speed. Results indicate that MLP and RBF based models with input of all selected weather parameters is able to estimate evaporation much precisely than LR and empirical approaches. It was found that higher accuracy may be obtained with multiple weather data input and low accuracy with only temperature input. It was observed that with temperature used as input the performance accuracy reduces in estimating evaporation with the selected models. However, neural network approach seems to produce better results as compared to statistical and empirical approach. The neural network based model RBF found more efficient in estimation of evaporation as compared to MLP. This study suggests that evaporation can be estimated by RBF model of a station, where there is no standard instrument available for its observation.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2022-03-14 19:52:11
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/58166
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 81, ##issue.no## 03 (2022): Journal of Scientific and Industrial Research
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/JSIR/article/download/58166/465589352