2. Thesis and Dissertations
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Item Study of Geomorphology and Dynamics of Shoreline Associated with Mulky-Pavanje Rivermouth, Dakshina Kannada Coast, Karnataka, India(National Institute of Technology Karnataka, Surathkal, 2013) Nagaraj, Gumageri; Dwarakish, G. S.The current thesis considered Mulky-Pavanje rivermouth and associated shoreline of about 12km length, lies between 13000'00''-13006'00'' North Latitude and 74044'00''- 74050'00'' East Longitude of Dakshina Kannda coast, Karnataka, India for short-term (<10 years), medium-term (10–60 years) and long-term (>60 years) shoreline changes. Beach survey, beach width, wave climate (height, period and direction) and wind parameters (speed and direction) and sediment sampling are gathered from nine locations (BS 1 – BS 9) to represent total 12 km shoreline, during the period from September 2009 to December 2011 for short-term change analysis. Short-term change analysis indicated that net accretion on the beaches towards the south of the rivermouth (BS 1–BS 5), whereas the north of the rivermouth experienced net erosion (BS 6–BS 9). For medium-term shoreline change analysis, rainfall and river discharges are obtained from Indian Meteorological Department for the periods 1985- 2011 and 1985-1998 respectively. The monsoonal storm directly induces rivermouth morphology to vary (BS 5–BS 6), adjacent beaches to suffer from erosion (BS1–BS 4 and BS 7–BS 9) and also leads drastic changes in wave climate and freshwater flow. During monsoon and post-monsoon periods, the rivers Mulky (North) and Pavanje (South) overflow, discharge sizeable quantities of sediments into the sea, whereas during the pre-monsoon periods, seawater enters into the rivermouth area leads sediment deposition and distribution on either side of the rivermouth. However, the discharge of the Mulky river is approximately two times more than that of Pavanje river. Because of the more flow in the Mulky river, which runs across the northern part of the rivermouth, the shoreline in the vicinity of rivermouth is predominantly shifting towards south. Additionally long-term shoreline change analyses are made through multidated satellite imageries and topomaps for the period 1912–2009. The long-term shoreline change analyses depicts that northern spit and rivermouth are shifting towards south during the period 1912–2009 and also observed that fluctuation of accretion and erosion pattern on southern side of the shoreline is highly significant as compared with northern side. The Mulky-Pavanje rivermouth being highly complex and dynamic, but it provides wide scope for developmental activities around it. Therefore Land use/Land cover changes are attempted by considering recentix decade, i.e 1998–2009 with the help of topographical map and remote sensing data. Land use/Land cover change analysis indicated that, because of development of urbanization and industrialization around the rivermouth, the built-up area has been drastically increased, while the other coastal related geological features such as beach vegetation, mangroves and river sand are drastically reduced during the period 1998– 2009. In addition, Artificial Neural Network (ANN) technique is used to model the very important parameters of the coastal engineering such as wave height and littoral drift, which cause coastal erosion in the study area. The developed NARX and FFBP models are evaluated using error statistics. In both cases the NARX model performed better than FFBP and proved that wave height and littoral drift are the direct responsible factors to cause erosion in the Mulky-Pavanje rivermouth and associated shoreline.Item Groundwater Level Forecasting using Radial Basis Function and Generalized Regression Neural Networks(National Institute of Technology Karnataka, Surathkal, 2013) D, Sreenivasulu; Deka, Paresh ChandraForecasting of groundwater levels is very much useful for efficient planning in integrated management of groundwater and surface water resources in a basin. Accurate and reliable groundwater level forecasting models can help ensuring the sustainable use of a watershed’s aquifer for both urban and rural water supply. The present work investigates the potential of two Neural networks, such as Radial Basis Function Neural Networks (RBFNN) and Generalized Regression Neural Networks (GRNN) in comparison to regular ANN models like Feed Forward Back Propagation (FFBP) and Non-Linear Regression Model (NARX) for modeling in Ground water level (GWL) forecasting in a coastal aquifer at western Ghats of India. Total 24 wells (both shallow and deep) located within the study area (microwatershed of Pavanje river basin) were selected covering around 40sqkm. Here, two different dataset such as weekly Time series GWL and Meteorological variables those recorded during the study period (2004-2011) were used in the analysis. Various performance indices such as Root Mean Squared Error (RMSE), Coefficient of Correlation (CC) and Coefficient of Efficiency (CE) were used as evaluation criteria to assess the performance of the developed models. At the first stage, the potential and applicability of RBF for forecasting groundwater level are investigated. Weekly time series groundwater level data upto four lagged data has been used as various input scenario where predicted output are one and two week leadtime GWL. The analysis has been carried out separately for three representative open wells. For all the three well stations, higher accuracy and consistent forecasting performance for RBF network model was obtained compared to FFBP network model. After confirming the suitability of RBF in GWL forecasting and with better accuracy over FFBP, the work has been extended further to consolidate the applicability of RBF in multistep leadtime forecasting upto six week ahead. In this study, six representative wells are covered for development of RBF models for six different input combinations using lagged time series data. Outputs are the predicted GWL upto six week. RBF models are developed for every well station and results are compared with Non linear regression model (NARX). It has been observed that for allGroundwater level Forecasting using Radial Basis Function and Generalized Regression Neural Networks, Ph.D Thesis, 2012, NITK, Surathkal, India viii the six well station, the higher and consistent forecasting performance by RBF network model in multi step week lead which consolidates the forecasting capability of RBF. The NARX model result shows poor performance. In the third stage, to examine the potential and applicability of GRNN in GWL forecasting, various GRNN models has been developed by considering the advantage of S-summation and D-summation layers for different input combinations using time series data. Weekly time series groundwater level data upto four lagged data has been used as inputs where predicted outputs are one week leadtime GWL. The analysis has been carried out separately for three representative open wells. GRNN models were developed for every well and best model results were compared with best RBF and FFBP with LM training algorithm models. The RBF and GRNN models are almost performed similarly in GWL forecasting with higher accuracy in all the representative well station. The poor performance of FFBP-LM model is also satisfactory but found inferior than both GRNN and RBF. After confirming the potential and applicability of GRNN and RBF in time series GWL forecasting with similar capability, the robustness, adaptability and flexibility characteristics of these two techniques are further investigated for suitability with cause and effect relationship. Here various meteorological parameters are used as causable variable and the GWL is used as output effect .Only GRNN models are developed in the present study as RBF was found with similar predicting performance in previous studies. Five various input combinations are used to obtain best results as one step leadtime output for three representative wells. In this case also, GRNN model is predicting groundwater level with higher accuracy and with satisfactory results. The GRNN model performance is compared to general ANN (FFBP) model and found outperforming FFBP performance. The result of the study indicates the potential and suitability of RBFNN and GRNN modeling in GWL forecasting for multistep leadtime data. The performance of RBFNN and GRNN were found almost equally good. Although accuracy of forecasted GWL generally decreases with the increase of leadtime, the GWL forecast were obtained within acceptable accuracy for both the models.Item Effect Of Data Preprocessing On The Prediction Accuracy Of Artificial Neural Network Model in Hydrologic Time Series(National Institute of Technology Karnataka, Surathkal, 2013) Banhatti, Aniruddha Gopal; Deka, Paresh ChandraThe accurate prediction of hydrological behavior in both urban and rural watershed can provide valuable information for the urban planning, land use, design of civil projects and water resources management. Hydrology system is influenced by many factors such as weather, land cover, infiltration, evapotranspiration, so it includes a good deal of stochastic dependent component, multi-time scale and highly non-linear characteristics. Hydrologic time series are often non-linear and non- stationary. In spite of high flexibility of Artificial Neural Network (ANN) in modeling hydrologic time series, sometimes signals are highly non-stationary and exhibit seasonal irregularity. In such situation, ANN may not be able to cope with non-stationary data if pre-processing of input and/or output data is not performed. Pre-processing data refers to analyzing and transforming input and output variables in order to detect trends, minimize noise, underline important relationship and flatten the variables distribution in a time series. These analyses and transformations help the model learn relevant patterns. Pre-processing techniques, which facilitate stabilization of the mean and variance, and seasonality removal, are often applied to remove non- stationary aspect in data used to build soft computing models. In this study, different data pre-processing techniques are presented to deal with irregularity components that exist in a hydrologic time series data of the Brahmaputra basin within India at the Pandu gauging station near Guwahati city and Pancharatna gauging station further 150km downstream of Pandu by using daily time unit and their properties are evaluated by performing one step ahead flow forecasting using ANN. Three different preprocessed datasets are used for the analysis. Various ANN models are generated by varying network internal architecture with different input scenarios. The model results were evaluated by using Root Mean Square Error (RMSE)and Mean Absolute Percentage Error (MAPE) and found that Logarithmic based pre-processing techniques provide better forecasting performance among various pre-processing techniques. The results indicate that detecting non-stationary aspect and selecting an appropriate preprocessing technique is highly beneficial in improving the prediction performance of ANN model.