Loading...
Thumbnail Image

Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

Browse

Search Results

Now showing 1 - 7 of 7
  • ThesisItemOpen Access
    Simulation of runoff and sediment yield from a hilly watershed using Soil and Water Assessment Tool (SWAT) and Wavelet Neural Network (WNN) models
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Jillani, Asima; Anil Kumar
  • ThesisItemOpen Access
    Meterological and hydrological drought characteristics of upper Ramganga Catchment in Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-08) Mishra, Alok Kumar; Anil Kumar
  • ThesisItemOpen Access
    Multilayer perceptron and single multiplicative neuron based artificial neural network rainfall - runoff models for a Himalayan watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2014-09) Singh, Praveen Vikram; Akhilesh Kumar
    Artificial Neural Network (ANN) has been widely used in the field of rainfall-runoff modeling. ANN models based on Multilayer perceptron and single multiplicative neuron were developed and verified for Khunt watershed in Almora, India to predict daily runoff. The daily data of rainfall and runoff of Khunt watershed of active monsoon period (18th June to 30th September) during years 2005-2009 were used for the training of both the models, whereas the data of years 2010 were used for validation and 2011 for the verification. The MLP-ANN was developed for various preprocessing techniques and the effect of preprocessing techniques on the performance of the model was considered. It was found that standardization technique provided the best results with smallest network topology for multilayer perceptron based ANN while using tan sigmoid function in the hidden layer and linear activation function in the output layer. It was also observed during the development of the models the multiple random generations of weights and biases are essential to reach the global minima along with practically feasible values of runoff. Model MLP4 having network structure as 6-5-1 with daily input series exhibits best results. Single multiplicative neural network is different from multilayer perceptron neural network as SMN-ANN is composed of multiplicative single neuron instead of multiple additive neurons. Single neuron model using multiplicative function strengthens non-linearity characteristic of the model and involves less parameters than those employed in MLP networks. SMN-ANN model was calibrated for different learning rates for daily input series and performed best for 0.5 learning rate. Qualitative performance of the model was assessed by the visual observation, whereas, quantitative performance was verified by estimating the values of various statistical indices such as coefficient of efficiency (CE), coefficient of determination (R2), index of agreement (d), normalized root mean square error (NRMSE), Integral square error (ISE), standardized mean absolute percentage error (SMAPE), peak difference percentage error (PDPE) and volumetric error (VE). SMN based ANN model performed very satisfactory for runoff prediction as the value of coefficient of efficiency (CE) was found to be 0.9511 while in case of MLP based ANN model it was found to be 0.8185 respectively. The value integral square error (ISE) was found to be less than 0.03 and the volumetric error was found within 10% for the whole data series. SMN-ANN model performed either at par or better than MLP-ANN model when applied on different data sets. At the same time, the SMN-ANN model is more user friendly as in its development the tedious and cumbersome process involved for the selection of appropriate network architecture is completely eliminated. Hence the SMN-ANN model provides an effective alternative to MLP-ANN model for rainfall–runoff modeling.
  • ThesisItemOpen Access
    Runoff disaggregation using artificial neural networks for a hilly watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-07) Panwar, Rajdev; Devendra Kumar
  • ThesisItemOpen Access
    Hydrologic modelling of a Himalayan watershed using soft computing, remote sensing and GIS techniques
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-07) Chanu, Sanjarambam Nirupama; Pravendra Kumar
  • ThesisItemOpen Access
    MLP and SMN based artificial neural networks model with linear and non-linear input for sediment yield prediction from upper Ganga basin
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-06) Singh, Saurabh; Akhilesh Kumar
    In this study attempts were made to develop sediment yield prediction models for the upper Ganga basin which comprises an approximate area of 2,31,127 km2. In the development of these models multivariate regression, multilayer perceptron based artificial neural network (MLP-ANN) and single multiplicative neuron based artificial neural network (SMN-ANN) techniques with linear and non-linear input of 20 years (1990-2010) and 30 years (1980-2010) durations have been used. An appropriate value of lag of input data which affects sediment outflow from the basin at any time was determined and following stepwise regression techniques, variables significant at 95% level were identified and used in model development. Large number of models were developed for various combinations of nature of input, number of neurons, epochs and transfer functions. Preliminary comparison lead to identification of six number of models applicable in each category of regression, MLP-ANN and SMN-ANN and three models specifically for monsoon season in each category. In order to distinguish and have an easy reference and better understanding of these models a set of nomenclature was developed and used. Accordingly, RM represents regression based model with prefixes of L, NL and LL which stand for linear, non-linear and log-linear input where as in case of ANN models MLP and SMN with suffixes of LM, NLM and LLM for linear, non-linear and log-linear input model respectively. The number 20 and 30 in a nomenclature signifies the use of data set of 20 years and 30 years respectively and the term MM represents that the model is developed considering monsoon season data set only. Developed models were evaluated quantitatively for their sediment yield prediction efficiency based on selected performance indicators such as, MAPE, RMSE, NRMSE, R2, ISE, CE, d and VE while qualitative performance was evaluated through visual comparison of observed and model predicted sediment yield through graphical representations. Performance comparison was made first within the category to identify a best performing model in a category. Then, performance comparison of category wise best performing models was carried out based on selected performance indices to identify an overall best performing model for sediment yield prediction in the study area both on annual basis and monsoon season basis. The comparative analysis indicated that in general SMN based ANN models provided better total annual sediment outflow prediction as compared to conventional MLP based ANN models in the upper Ganga basin with linear input. The SMN20LM with 20 epochs while SMN30LM with 80 epochs and log-sigmoid as an activation function provided best sediment yield prediction. The RMSE and VE values for these models were obtained as 1.467tons/s & -3.746% and 1.459tons/s & -2.133% respectively. MLP-ANN models were found working satisfactorily for total annual sediment outflow prediction when applied in the study area considering 20 years and 30 years data series with non-linear input. Model architectures of 8-2-1 for MLP20NLM and 5-9-1 for MLP30NLM with log-sigmoid transfer function at hidden layer and tan-sigmoid transfer function at the output layer for both these models provided best prediction of total annual sediment yield. The values of RMSE and VE were obtained as 1.032 tons/s & -3.182% and 0.728 tons/s & -5.348% with index of agreement as 0.996 and 0.998 respectively for these models. With log transformed input SMN20LLM at 3 epochs where as SMN30LLM at 2 epochs provided the best results with respective RMSE and VE values of 2.498 & -4.378% and 2.245 tons/s & 9.975% with coefficient of efficiency 0.90 and 0.919 and index of agreement as 0.975 & 0.977 respectively. In case of monsoon season SMN based ANN models were found to be performing better in each category. On the basis of overall comparison of category wise selected models, it was found that the MLP based ANN model with 20 years non-linear input (MLP20NLM) while SMN based ANN model with 30 years linear input (SMN30LM) provided the best sediment prediction efficiency. The calculated values of RMSE and VE for these two models were obtained as 1.032 tons/s & -3.182% and 1.459 tons/s & -2.133% respectively. Similarly, a comparison of category wise selected monsoon models revealed that single multiplicative neuron based artificial neural network model (SMN-ANN) with 30 years monsoon data in its non-linear form (SMNNLMM30) provided the best sediment outflow prediction of 9,50,48,901.16 tons as against 9,51,82,452.0 tons of observed sediment yield for monsoon season which resulted in the volumetric error value of 0.140%. In both the cases, therefore, it can conveniently be concluded that these models are applicable very effectively in the study area as model predicted total sediment outflow was found to be very close to the total observed sediment outflow.
  • ThesisItemOpen Access
    Runoff and sediment yield modelling using soil and water assessment tool for management planning of Mojo Watershed, Ethiopia
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-01) Gonfa, Zelalem Biru; Devendra Kumar
    Physically based Soil and water Assessment Tool (SWAT) model was setup and evaluated to assess runoff and sediment yield from Mojo watershed (2017.21 km2) situated in Central Oromia Regional state, Ethiopia. In this study for stream flow simulation parameters involving surface runoff (CN2.mgt) and ground water (ALPHA_BNK.rte) are found the most sensitive parameter and the parameters representing soil (USLE_K.sol) and surface runoff (SPCON.bsn), were found more sensitive for sediment yield simulation. A good agreement between observed and simulated discharge were observed, which was verified using both graphical technique and quantitative statistics. The value of R2=0.83, NSE=0.82, RSR=0.42 and PBIAS=10.5 obtained during calibration and R2 value 0.77, NSE value 0.75, RSR value 0.50 and PBIAS 9.8 obtained during validation as well as the uniformly scatter points along the 1:1 line during calibration and validation justify that the model is very good in simulating runoff. For sediment yield the computed statistical indicators R2=0.76, NSE=0.75, RSR=0.50 and PBIAS = 8.10 were obtained during calibration and during validation the computed statistical indicators were found 0.79 for R2, 0.71 for NSE, 0.54 for RSR and 35.83 for PBIAS. Based on SWAT model output a multi-objective linear programming model was developed to solve several conflicting objectives and to optimize simultaneously considering minimizing soil erosion and maximizing benefit as an objective function and area under different Land use as a constraint. Accordingly, a reduction of dry land farming by 18.45% and increasing the current rangeland 946.36 ha to 15419.74 ha and 45.96 ha under irrigated agriculture to 25526.69 ha would increase the net income and minimize soil erosion from the watershed by 29.91% and 16.14% respectively without making much difference of the current forest land. Furthermore, a decision support system and methodology was developed for the identification land capability classification of each HRUs and to suggest various watershed management practices based on the identified land capability classification.