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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.

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2017 - 201912020 - 20221
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Subject: Civil Engineering × Start date: 2014 × Thesis Type: Ph.D ×
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    ThesisItemOpen Access
    Spring water quality evaluation and prediction of water quality index using Artificial Neural Network in Bageshwar block of Bageshwar district, Uttarakhand state
    (G. B. Pant University of Agriculture and Technology, Pantnagar, 2022-03) Mohd Azam; Prasad, Jyothi
    Springs are a critical resource for the people of Bageshwar block of Bageshwar district, but are currently facing threats as a result of rapid urbanization and climate change. Here the people mainly depend on springs as the primary source of water. As a result, majority of people in Bageshwar become reliant on springs to satisfy their daily demand for water. The water quality of these springs is declining because of numerous anthropogenic activities, such as construction, deforestation, etc. The current study evaluated the drinking water quality of Bageshwar block springs, which is located in Uttarakhand's eastern Kumaon region between latitudes 29°41'60"N to 29°53'60"N and longitudes 79°35'60"E to 80°5'60"E and has an average elevation of 882.30 m above mean sea level. Spring water samples were collected and measured discharge between 2019- 2021 and checked for various physico-chemical and bacteriological parameters from eleven springs namely Banri (BS1), Manikhet (BS2), Darsu Aare (BS3), Kukudagaad (BS4), Kamedi (BS5), Shri Naula Dhaara (BS6), Bilauna (BS7), Bhaniya Dhaar-1 (BS8), Bhaniya Dhaar-2 (BS9), Bhitaal Gaon (BS9) and Nye Basti Chaurasi (BS11) in Bageshwar block which are conveniently accessible. Data on the spring inventory was obtained on site by interviewing the local spring users. In this study, all spring discharges were measured using the volumetric method and then various physiochemical and bacteriological water quality parameters tests were performed using BIS standard methods. The DEM of the study area was analyzed to generate the channel network map were created using QGIS 3.16. The Weighted Arithmetic Water Quality Index (WAWQI) method was applied to the eleven springs of the study area. Prediction of Water Quality Index is also done using Artificial neural Network (ANN) model of a fully-connected, feed-forward backpropagation MLP network in MATLAB. During the study period, it was found that the most of the springs were slightly alkaline. The EC, TDS, Chloride, Potassium, sodium, Residual Free Chlorine, turbidity, Iron, Nitrate and calcium concentration of Bageshwar block for all springs were within the acceptable limits for all the springs. The total hardness (200 mg/l), fluoride (1.0 mg/l) and magnesium concentration (30 mg/l) in most of the springs exceeded the acceptable limits with few springs falling below the acceptable limit. However, the total hardness (200-600 mg/l), fluoride (1.0-1.5 mg/l), total alkalinity (200-600 mg/l) and magnesium concentration (30-100 mg/l) in all the springs were within the permissible limit of BIS 10500 (2012) and WHO (2011). After confirmatory test of MPN index method it was found that the E coli was present in BS11 spring. After evaluating Water Quality Index, with Weighted Arithmetic Water Quality Index (WAWQI) method, it has been found that the overall quality of spring water for all spring sites found to potable. It could be concluded that the statistical correlation analysis was a suitable technique for finding correlation between different physico-chemical parameters. The WQI variable was modelled through ANNs using 15 water quality parameters as input variables. Subsequently, the models with fewer variables were tested with different scenarios and reduced up to the five water quality parameters (i.e., Q, Total Alkalinity, fluoride, iron, and magnesium). The overall performance of the ANN models was evaluated based on RMSE and R2. It was observed that the number of independent input parameters were reduced from fifteen to five with less effect on the model performance. Hence it is recommended that the best-performing MLP ANN model was M11 with five input parameters having RMSE of 0.7349 and reasonably high coefficient of determination-R2 (0.9625). The regression equation, y = 0.9141x + 1.1273, represents the relationship between the predicted and actual water quality index values. In purview of the present study, it is therefore suggested that the five-input parameter based multi-layer perceptron (MLP) artificial neural network (ANN) model will be the best suited to evaluate the water quality of the Bageshwar block springs.
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    ThesisItemOpen Access
    Characterization of strength behaviour of model material (sand -bentonite) mixed with fibre
    (G.B. Pant University of Agriculture and Technology, Pantnagar (Uttarakhand), 2017-01) Sharma, Subir Kumar; Gupta, S.S.
    One of the major problems faced by geotechnical engineers is to improve the engineering properties of clayey soil. The technique of improving engineering properties of soil is termed as soil stabilization or modification. In present study, sand procured from the bank of River Gaula at Kathgodam, district, Nainital, (Uttarakhand) was mixed with different proportions of sodium bentonite. A number of test were conducted at various percentages bentonite mix with sand such as S100 B0, S95 B5, S90 B10, S85 B15, S80 B20 ,S75 B25, S70 B30, S65 B35 and S60 B40. Physical properties of Gaula sand and bentonite were determined in the laboratory. Specific gravity, optimum moisture content (OMC), maximum dry density (MDD), porosity and void ratio , Atterberg limits, standard proctor test, CBR tests and strength parameters were studied at different proportion of sand bentonite mixes. Unsoaked CBR value increased approximately 100% (more than two times the initial value) for mix S100B0 to S80B20 as the recron-3s fibre mixed randomly from 0.1% to 2.0% by weight of total mix. For mix S75B25 to S60B40 unsoaked CBR value increased about 2.4 to 3.8 times the initial value. Similarly soaked CBR value increased about 2.2 to 3.72 times the initial value when recron-3s fibre mixed randomly from 0.1% to 2.0% by weight of total mix. Shear strength parameters, angle of internal friction and cohesion increased with increasing bentonite proportion in the mixes with and without mixing of recron-3s fibre from 0.1% to 2.0% by weight of total mix. This shows shear strength of the mix increased upon mixing the recron-3s fibre.The addition of recron-3s fibre with black cotton soil can be done resulting improvement of shear strength parameters and CBR values, leading to the economic solution to the pavement constructed on the poor subgrade i.e. like black cotton soil.