Sirisha Adamala

Evapotranspiration Modeling Using Second-Order Neural Networks

AbstractThis study introduces the utility of the second-order neural network (SONN) method to model the reference evapotranspiration (ET0) in different climatic zones of India. The daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation from 17 different locations in India were used as the inputs to the SONN models to estimate ET0 corresponding to the FAO-56 Penman-Monteith (FAO-56 PM) method. With the same inputs, for all 17 locations the first-order neural networks such as feed forward back propagation (FFBP-NN) models were also developed and compared with the SONN models. The developed SONN and FFBP-NN models were also compared with the estimates provided by the FAO-56 PM method. The performance criteria adopted for comparing the models were root-mean-squared error (RMSE), mean-absolute error (MAE), coefficient of determination (R2), and the ratio of average output to average target ET0 values (Rratio). Based on the comparisons,...

Generalized Quadratic Synaptic Neural Networks for ETo Modeling

This study aims at developing generalized quadratic synaptic neural (GQSN) based reference evapotranspiration (ETo) models corresponding to the Hargreaves (HG) method. The GQSN models were developed using pooled climate data from different locations under four agro-ecological regions (semi-arid, arid, sub-humid, and humid) in India. The inputs for the development of GQSN models include daily climate data of minimum and maximum air temperatures (Tmin and Tmax), extra terrestrial radiation (Ra) and altitude (alt) with different combinations, and the target consists of the FAO-56 Penman Monteith (FAO-56 PM) ETo. Comparisons of developed GQSN models with the generalized linear synaptic neural (GLSN) models were also made. Based on the comparisons, it is concluded that the GQSN and GLSN models performed better than the HG and calibrated HG (HG-C) methods. Comparison of GQSN and GLSN models, reveal that the GQSN models performed better than the GLSN models for all regions. Both GLSN and GQSN models with the inputs of Tmin, Tmax and Ra performed better compared to other combinations. Further, GLSN and GQSN models were applied to locations of model development and model testing to test the generalizing capability. The testing results suggest that the GQSN and GLSN models with the inputs of Tmin, Tmax and Ra have a good generalizing capability for all regions.

Generalized wavelet neural networks for evapotranspiration modeling in India

Abstract This study focuses on the application of generalized wavelet neural network (GWNN) models corresponding to the FAO-56 Penman Monteith (FAO-56 PM), Turc, and Hargreaves (HG) methods for estimating daily reference evapotranspiration (ETo). The daily pooled climate data from 15 different locations under 4 different agro-ecological regions (AERs: semi-arid, arid, sub-humid, and humid) in India are used as an input to GWNN models. The inputs include three combinations of climate data (minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation) and the target consists of the FAO-56 PM estimated ETo. Further, the GWNN models were applied to 15 individual model development locations and 10 different model testing locations to test the generalizing capability. Comparison of developed GWNN models was made with the classic generalized artificial neural network (GANN), generalized linear regression (GLR), generalized wavelet regression (GWR), and corresponding conventional methods to test the superiority of one model over the other. Results reveal that the GWNN models followed by GANN models performed better than GWR and GLR models for four AERs. The testing results suggest that the GWNN and GANN models have better generalizing capabilities than the GWR and GLR for almost all region locations.

Development of Generalized Higher-Order Neural Network-Based Models for Estimating Pan Evaporation

It is common practice to develop artificial neural network models using location-based single dataset for both the training and testing. Based on this procedure, the developed models may perform poorly outside the training location. Therefore, this study aims at developing generalized higher-order neural network (GHNN) models for estimating pan evaporation (E p) using pooled climate data of different locations under four agro-ecological regions in India. The inputs for the development of GHNN models include different combinations of daily climate data such as air temperature, relative humidity, wind speed, and solar radiation. Comparisons of developed GHNNs were made with the generalized first-order neural network (GFNN) and generalized multi-linear regression (GMLR) models. It is concluded that the GHNNs along with GFNNs performed better than the GMLR models. Further, GHNNs were applied to model development and model testing locations to test the generalizing capability. The testing results suggest that the GHNN models have a good generalizing capability.

Case Study on Performance Evaluation of Drip Irrigation Systems in Selected Villages of Guntur District, Andhra Pradesh, India

Drip irrigation (also known as trickle irrigation or micro irrigation) is an irrigation method, which minimizes the use of water and fertilizer by allowing water drop by drop slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through a network system of valves, pipes, tubing, and emitters. Modern pressurised irrigation technology was invented in Israel by Simcha Blass and his son Yeshayahu. Instead of releasing water through tiny holes, which blocks easily by tiny particles, water was released through larger and longer passageways by using slow water velocity inside a plastic emitter. The first experimental system of this type was established in 1959, when Blass partnered with Kibbutz Hatzerim to create an irrigation company called Netafim. Together they developed and patented the first practical surface drip irrigation emitter. This method was very International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 2 (2017) pp. 437-445 Journal homepage: http://www.ijcmas.com