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Theses

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2016 - 20174
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Subject: Soil and Water Engineering × End date: 2017 × Start date: 2013 × Thesis Type: M.Tech. ×
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Now showing 1 - 4 of 4
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    ThesisItemOpen Access
    Pollution assessment of river Ganga segment in Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Dabral, Ashish; Kashyap, P.S.
    Soil erosion due to rain and wind action is a serious problem in India. Its negative impacts include reduction in soil productivity, silting of dams and reservoirs, deficits in water availability, pollution of water courses, serious damages to properties by soil-laden runoff, and desertification of natural environments. In the present study, chemical and physical parameters of the river were observed. The river Ganga segment in Uttarakhand was taken as study area from Devprayag (30.140N, 78.590E) to Balawali (29.640N, 78.10E). From the study area, a total of twelve locations were selected and water samples were taken in February and June. The classification of the locations of the samples of both the months for the utilization of water for various purposes like drinking water source without conventional treatment but after disinfection, fish culture and wild life propagation and irrigation and industrial cooling or controlled waste disposal was done for useful interpretation of which water could be used for what purposes according to desirable and permissible limits of pH, EC, TDS, free CO2, chloride, total acidity, calcium hardness, total hardness and magnesium hardness.
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    ThesisItemOpen Access
    Suspended sediment yield modelling using artificial neural networks
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-06) Kushwaha, Daniel Prakash; Devendra Kumar
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    ThesisItemOpen Access
    Assessment of geomorphologic characteristics and soil erosion status for prioritization of hilly watersheds using remote sensing and GIS
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-06) Kandpal, Himanshu; Anil Kumar
    Soil erosion due to rain and wind action is a serious problem in India. Its negative impacts include reduction in soil productivity, silting of dams and reservoirs, deficits in water availability, pollution of water courses, serious damages to properties by soil-laden runoff, and desertification of natural environments. The present study was undertaken in the Bino sub-catchment of Ramganga river catchment, covering an area of 294.81 km2 with the main objective of prioritizing watersheds based on gross soil erosion and geomorphological parameters. The Bino sub-catchment consist of 9 watersheds. Different thematic maps of the study area were prepared using SOI toposheets (1:50,000 scale) and satellite imagery of study area with the help of ArcGIS software. Gross soil erosion was computed based on RUSLE. Gross soil erosion was integrated with the geomorphological parameters viz. bifurcation ratio, drainage density, stream frequency, texture ratio, mean length of overland flow, form factor, circulatory ratio, compactness coefficient and elongation ratio to prioritize the watersheds for erosion risk assessment. Gross soil erosion and all the geomorphological parameters were ranked according to their erosion hazards. On the basis of compound rank of factors of gross soil erosion and geomorphological parameters, the watershed W1 got the highest and W9 got the least priority in the study area. It was observed from priority list that one watershed were under very high priority category, one watersheds were under high priority category, four watersheds were under medium priority category, two watersheds were under low priority and the remaining one watershed were under very low priority category. Required soil and water conservation measures should be implemented in the watersheds as per the assigned priority.
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    ThesisItemOpen Access
    Suspended sediment yield modelling using artificial neural networks
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-06) Kushwaha, Daniel Prakash; Devendra Kumar
    Eight artificial neural networks based models were developed to predict daily suspended sediment concentration for the Baitarani river at Anandpur and Champua stations using daily discharge and daily suspended sediment concentration. The 30 years data (June 1977 to September 2006) used in this study was divided into two sets viz. a training set (1977-1996) and a testing set (1997-2006). ANNs models were calibrated by using multilayer feedforward back propagation neural networks with sigmoid activation function and Levenberg-Marquardt (L-M) learning algorithm. The performance of the developed models was evaluated qualitatively and quantitatively. In qualitative evaluation of models, the observed and the computed suspended sediment concentration were compared using sediment hydrographs and scatter plots during testing period. Akaike’s information criterion (AIC), correlation coefficient (r), mean square error (MSE), root mean square error (RMSE), minimum description length (MDL), coefficient of efficiency (CE) and normalized mean square error (NMSE) indices were used for quantitative performance evaluation of the models. Results on the basis of qualitative and quantitative evaluation indicate that M-6 model with (7-5-5-1) network architecture is better than all models at Champua station and M-1 model with (2-7-7-1) network architecture is better than all models at Anandpur station.