2. Thesis and Dissertations
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Item Optimization of Vessel Turnaround Time at a Seaport with a Special Reference to New Mangalore Port Trust(National Institute of Technology Karnataka, Surathkal, 2019) K, Dayananda Shetty; Dwarakish, G. S.Turnaround time of a vessel in a seaport exhibits the capability and ability of a port in providing efficient and effective services. Ship turnaround time is one of the most significant Port performance indicator. This is the total time, spent by the vessel in port, during a given call. It is the sum of waiting time, berthing time, service time (i.e., ship’s time at berth) and sailing delay. Indian ports play a crucial role in trade and economy, as 95% of merchandise trade is handled by ports. However, port turnaround time remains a key problem. It is significantly slower when compared to ports in other developing countries, being several times higher than for ports in China, Singapore and Malaysia. It is estimated that about 40% of ships time is spent in ports. The main contributing factors in high turnaround time of a vessel in a sea port are the port congestion, loading/unloading speed, method of cargo handling, general operational delays, strikes, ship catastrophe, pilotage and mooring time and delay due to weather. To reduce the turnaround time of a vessel in a seaport, the port processes are to be streamlined and capacity augmented. This research is aimed at identifying the factors that are responsible for turnaround time of vessels at New Mangalore Port. As the present day vessels are of very large size to take advantage the economy of scale requires huge investment. The return on investment depends on the earning of vessel during her entire life. The voyage time of a vessel is the only earning period of the vessel, whereas the time spent in seaports is expenditure for the ship owners. Therefore every ship owners expect a very low turnaround time to get maximum benefits. The turnaround time is a function of port operations and port facilities. Currently, it is not possible to determine the significant factor(s) that influence port performance, in terms of turnaround time. The primary data of vessel arrival/departure in New Mangalore Port Trust (NMPT) recorded at Vessel Traffic Management System (VTMS) are used for the present study. This study revealed that more than 120 factors affect the turnaround time of the vessel in a seaport. The factors attributable to high turnaround time at NMPT are mainly due to the precommencement and post-commencement documentation/custom formalities and the total time lost during loading unloading process. Non-availability of berths in case of liquid bulk cargo and non-availability of equipment’s and lack of mechanization inii the dry bulk cargo handling also contributes to the increased turnaround time of vessels. The weightage of these factors in contributing high to turnaround time are analysed through the developed optimisation tool and total time consumed by the top 20 critical factors are calculated month on month basis. The sensitivity analysis shows that in the year 2018-2019 on an average of 1350 hours per month is lost which is around 17.50% of the total monthly turnaround time of the all vessels calling on to the port during the year in consideration. The major constrains which contribute to this increased turnaround time are the total time lost during loading and unloading operations, documentation procedures, custom clearance, survey of the cargo and the time lost due to non-port account. The sampling of cargo and lab test for quality assurance before loading and unloading the cargo by the concerned authority also contributes to the delay in turnaround time of the vessel. Weather constraints and some of the port constraints like shifting of vessel from one berth to the other for draft requirements, idle time at berth are some of the factors which also contribute to the increased vessel turnaround time in NMPT. Thus, the outputs of the optimization tool will able to help the port management to verify the problems in an identified area on a real time basis and to come out with better solutions in providing services and improving the port activities, to ensure lower vessel turnaround time and increased port productivity and efficiency. It is possible to tackle all the delay causing factors by the port management in providing efficient and effective services and increased port productivity, with the aim to achieve optimum port performance. The model can also be useful in generating various Management Information System (MIS) outputs and Port Performance Indicators (PPI) with respect to the category of cargo handled, Berth wise, on daily/monthly/yearly basis.Item Estimation of saturated hydraulic conductivity in spatially variable fields using various soft computing techniques(National Institute of Technology Karnataka, Surathkal, 2019) More, Satish Bhaurao; Deka, Paresh ChandraSaturated hydraulic conductivity (Kfs) is one of the dominant and most essential soil hydrology characteristics, for understanding and duplicating various hydrological processes having environmental importance. Its estimation by (laboratory / field) method is cumbersome, time consuming and costly. In addition, the results may not be representative due to spatial variability of Kfs. This attracted researcher to address this problem by developing pedotransfer function (PTF), which estimate saturated hydraulic conductivity by using routinely measured soil properties. Objective of this study is to investigate the spatial variability of saturated hydraulic conductivity under different land use land cover by using Guelph permeameter, and to develop PTF for estimating Kfs, from soil index properties, by using soft computing techniques and thus evaluate the performance of these techniques by using statistical tests. Study is carried out at Solapur, India. Three sites (0.76ha) were identified which are having different land use land cover. The site is divided in to small grids of 10m X 10m, and observations were taken at corner of each such grid, at 15cm, 30cm and 45cm depth. In situ and laboratory tests were carried out to estimate Kfs and other basic index properties of soil. Observed data (In situ as well as laboratory) were preprocessed and then used for modeling purpose. Total dataset (Three sites, three depth with 100 sampling points; so overall 900 sample data) is segregated into six sub dataset (college, Mulegaon, Punanaka, 15cm, 30cm and 45cm). Each subset consisting of 300 observations is further split into two parts in six different ways (90% + 10%, 85% + 15%, 80% +20%, 75% + 25%, 70% + 30%, and 67% + 33%) to train the models and validate it, the combination which gives good results during training and validation is selected. For checking performance of model, various statistical parameters such as correlation coefficient (R), mean relative error(MRE), root mean square error (RMSE), normalized root mean square error (NRMSE) and Nash Sutcliffe efficiency (NSE) has been made. Scatter plots were used to evaluate the accuracies of the models. For deciding best model these checks are used, Value of R ~ 1, value and NSE ~ 1, MRE close to zero, and NRMSE is close to zero. Scatter plot point distribution should be around and close to 1:1 line. Maximum value of log saturated hydraulic conductivity was observed at Punanaka (3.842 m yr-1) and minimum value at college site (0.002myr-1). Standard deviation for Kfs was least at Punanaka (0.598m yr-1) and was maximum at college site (0.804myr- 1). Porosity has strong positive Correlation coefficient 0.9 whereas bulk density has strong negative correlation of 0.9. The performance of ELM model at all six subsets was found performing better than SVM and ANFIS model. NRMSE values of ELM model (training: testing) were found [0.02:0.06, 0.07:0.09, 0.02:0.07, 0.03:0.08, 0.07:0.10 and 0.008:0.04] at college site, Mulegaon site, Punanaka site, 15cm depth, 30cm depth and 45cm depth respectively. Saturated hydraulic conductivity was found varying spatially, land use land cover has influence on Kfs and it found declining with depth. College station has shown more variability in Kfs also variation of Kfs was found more at 45cm depth. Maximum Standard deviation was found in college site and minimum standard deviation was found at Punanaka site. Variability of porosity, bulk density and particle density was found insignificant in logarithmic scale. Soil particle size was found declining with depth. Porosity has shown strong positive correlation with Kfs, whereas bulk density has shown strong negative correlation. Performance of ELM model was found excellent in all six sub data set both during training and testing. Performance of SVM and ANFIS was not found satisfactory during testing although they are within acceptable accuracy. Saturated hydraulic conductivity has shown spatial variation; it was varying along depth as well as lateral and longitudinal direction, generally Kfs was found decreasing with depth. Kfs was found decreasing down the slope. ELM model outperformed other two models tried in this study (ANFIS and SVM). Texture of soil was founddeclining from coarse to fine with depth at majority of sampling location. Mean value of Kfs was found more at Punanaka site (15cm depth) as compared to other two sites.Item Hydroelastic Analysis of of Very Large Floating Structure (VLFS) using Boundary Element Approach(National Institute of Technology Karnataka, Surathkal, 2019) Shirkol, Anoop. I.; Nasar, T.Hydroelasticty is a subject of interest in marine science and technology involving the mutual interaction of water waves and elastic bodies. It is a branch which deals with the elastic deformation of bodies which is in contact with liquids. Interdisciplinary subjects like this require the knowledge of structural mechanics, fluid mechanics, concepts of water wave propagation and boundary conditions. In this thesis, a numerical procedure has been proposed to analyze the equation of motion of the elastic plate which is having a shallow draft, L/d ≤ 1/20 (small thickness) with arbitrary geometry subjected to monochromatic gravity waves.The numerical model is capable of investigating the Very Large Floating Structure (VFLS) at finite (0.05 ≤h/λ≤ 0.5) and infinite (h/λ≤ 0.5) water depths. Herein, VLFS is considered to behave as thin elastic plate due to its dimensions. VLFS of rectangular, triangular and trapezoidal geometries are considered and elastic motion or vertical deflections of these shapes have been studied. A hybrid numerical model which combines Boundary Element Method (BEM) and Finite Element Method (FEM) is developed and used to solve fluid structure interaction between the elastic thin plate and water wave. A Higher Order Boundary Element Method (HOBEM) has been adopted in order to maintain the same order basis function and contains the same nodes between BEM and FEM. Two equations have been derived to develop the relationship between the displacement of the plate and the velocity potential under the plate. The first equation is derived from the equation of motion for the plate and is solved by Finite Element Method (FEM) to extract the displacement of the floating structure. The second equation is from water wave theorywhich is based on Boundary Integral Equation (BIE) that relates the displacement of the floating plate and velocity potential using free-surface Green’s function. A modified Green’s function which differs from the bygone Green’s function has been developed by using Bessel’s, Hankel and Struve functions of order zero. Both the equations are solved simultaneously to get the displacement of floating elastic plate and velocity potential. The results obtained are validated with Wang and Meylan (2004). The performance of the developed model is examined by checking the convergence rate and simulation time.It is learnt that the model gives its better performance in finite depth, whereas, its performance in infiniteii depth lags by an average of 20% in simulation time than the results obtained by Wang and Meylan (2004).It is concluded that the model works better in finite water depth for rectangular and trapezoidal platesItem Feature Extraction Strategies based on Mathematical Morphology for the Analysis of Remotely Sensed Imagery(National Institute of Technology Karnataka, Surathkal, 2019) C. A, Rishikeshan; Ramesh, H.The thesis evolves on the development of novel feature extraction methods for the analysis of remotely sensed images which are enabled to enhance the robustness and the generalization properties of the feature extraction system. Recent developments in optical data sensors mounted on-board of both space-borne and airborne earth observation platforms have led to increasing volume, acquisition speed and a variety of sensed images. Therefore the feature extraction from remotely sensed imageries is a major concern and challenge for the photogrammetry, remote sensing, and GIS communities. The extensive survey of literatures expose the shortcomings overlooked for the existing approaches utilized in the feature extraction of remote sensing images. The automated extraction of features from the remotely sensed images has been an active area of research for over a decade due to its substantial role in several application areas viz. urban planning, transportation navigation, traffic management, emergency handling, etc. Although the concept of feature extraction is relatively simple, the reliability and accuracy remains a major challenge. With advanced imaging technologies, there is an augmented demand for developing new approaches which can exhaustively explore the information embedded in remote sensing images. The past studies evidenced mathematical morphological tools as best suited for the potential exploitation of the spatial information in the remote sensing imageries. Priorly, mathematical morphology was applied only for the interpretation of binary images. However, it was extended to analyze grey scale and colour images. The thesis presents different spatial feature extraction methods which are developed based on mathematical morphology for the analysis of remote sensing optical images addressing to different applications such as urban feature detection, waterbody extraction, crop field boundary extraction and shoreline extraction. The morphology based feature extraction algorithms developed are effective and contribute to the interpretation of high resolution remotely sensed images.This automatic, scalable, and parallel processing methods can be used to analyze colossal remote sensing data within the selected classification schemes of remote sensing image system. The proposed methodologies contribute to the operational use of remote sensing datasets in manyii practical applications related to monitoring and management of environmental resources. In this thesis, a novel approach is presented for extracting shoreline from remotely sensed images. Shoreline extraction is inevitable for several studies such as coastal zone management, coastline erosion monitoring, GIS database updating, watershed definition, flood plain mapping and the evaluation of water resources. Multiple techniques are proposed for the extraction of different types of waterbodies such as lakes, rivers and glacier lakes. MM techniques have been exploited for the extraction of crop field boundaries from multiple satellite imageries. UAV driven images are beneficial as they facilitate a comprehensive description of the scenes, and concurrently require pertinent image processing techniques to exploit the geometrical information from the image datasets. This study introduces two innovative feature extraction methods for UAV and satellite images The novel feature extraction techniques proposed in the thesis have been investigated and experimented in different datasets to test their degree of performance. The experimental investigation performed with the developed techniques for analysis of remotely sensed images are noted for its improved accuracy when compared against other state of the art methods.Item Assessment of spatio-temporal variability of streambed hydraulic conductivity: A case study in the Pavanje river, India(National Institute of Technology Karnataka, Surathkal, 2019) N, Sujay Raghavendra; Deka, Paresh ChandraThe hydro-geological properties of streambed together with the hydraulic gradients determine the fluxes of water, energy and solutes between the stream and underlying aquifer system. Uncertainty in stream-aquifer interactions arises from the inherent complex-nested flow paths and spatio-temporal variability of streambed hydraulic properties. The estimation and modeling of streambed hydraulic conductivity (Ks) is an emerging interest due to its connection to water quality, aquatic habitat, and groundwater recharge. Fragmenting streams with dams, diversions, and less frequently road culverts disrupt the longitudinal connectivity and capacity of a stream. Dam induced sedimentation affects hyporheic processes and alters substrate pore space geometries in the course of progressive stabilization of the sediment layers. The present study reports the spatial and temporal variability of streambed hydraulic conductivity along the stream reach obstructed by two Vented Dams in sequence. A detailed field investigation of streambed hydraulic conductivity using Guelph Permeameter was carried out in an intermittent stream reach of the Pavanje river basin located in the mountainous, forested tract of Western Ghats of India. Arriving at realistic statistical and spatial inference based on in-situ data collected is challenging, considering the possible sediment sources, processes, and complexity. Statistical tests such as Levene’s and Welch’s ttests were employed to check for various variability measures. The strength of spatial dependence and the presence of spatial autocorrelation among the streambed Ks samples were tested by using Moran’s I statistic. The measures of central tendency and dispersion pointed out reasonable spatial variability in streambed Ks distribution throughout the study reach during two consecutive years 2016 and 2017. The streambed was heterogeneous with regard to hydraulic conductivity distribution with high-Ks zones near the backwater areas of the vented dam and low-Ks zones particularly at the tail water section of vented dams. Dam operational strategies were responsible for seasonal fluctuations in sedimentation and modifications to streambed substrate characteristics (such as porosity, grain size, packing etc.), resulting in heterogeneous streambed Ks profiles. The channel downstream of vented dams contained significantly more cohesive deposits of fine sediment due to the overflow of surplus suspendedii sediment-laden water at low velocity and pressure head. The statistical test results accept the hypothesis of significant spatial variability of streambed Ks but refuse to accept the temporal variations. Advanced geo-statistical techniques offer a wide range of univariate or multi-variate interpolation procedures such as kriging and variogram analysis that could be applied to these complex systems. The deterministic and geostatistical approaches of spatial interpolation provided virtuous surface maps of streambed Ks distribution. The Moran’s I index approved the presence of spatial dependence in the heterogeneous streambed Ks samples. Interpolation maps of Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF) were more accurate than the krigged surface maps; however, the prediction uncertainty was lower around the sampled values in ordinary kriging estimates compared to deterministic methods. In-situ measurement of streambed hydraulic conductivity all along the length of the stream may not be an ideal and cost-effective way. Hence, the soft computing approaches could be applied to induce a rule based relationship for estimating the values of streambed hydraulic conductivity at unmeasured locations using representative georeferenced neighborhood data. The artificial intelligence (AI) based spatial modeling schemes were tested to predict the spatial patterns of streambed hydraulic conductivity. The geographical coordinates (i.e., latitude and longitude) of the sampled locations from where the in-situ hydraulic conductivity measurements were made were used as model inputs to predict streambed Ks over spatial scale using artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) paradigms. The statistical measures computed by using the actual versus predicted streambed Ks values of individual models were comparatively evaluated. The AI based spatial models provided superior spatial Ks prediction efficiencies with respect to both the strategies/schemes considered. The SVM model was found to predict reasonably accurate streambed Ks patterns.Item Modeling of River-Aquifer Interactions: A Top-Down Approach(National Institute of Technology Karnataka, Surathkal, 2019) S, Harish Kumar; Nagaraja, M. K.Surface water interacts with groundwater in many types of physiographic and hydrogeological conditions. The heterogeneity in hydrological processes over a catchment affects the location, time and extent of interaction phenomenon. Exchange of water varies spatially and temporally due to the effect of natural and anthropogenic factors. It is essential to identify and quantify the surface water and groundwater (SW-GW) exchange, since the quantity and quality of these resources affect each other. The SWGW interactions are characterized at various scales of flow systems such as regional, intermediate and local scale. Driving forces influence the SW-GW exchange depending upon different flow scales but the challenge of the present research is to bring out its relevance. In the past, studies related to SW-GW interactions using integrated models are not dealt in detail for smaller scales. Small-scale processes may seem insignificant when accounted for a larger perspective of consideration. Therefore, an effort is put to analyze the effect of driving forces on a larger scale and a small scale in the present study. The objective of the present study is to investigate the surface water and groundwater interactions at two different hydrological extents of a catchment. The present study proposes a new approach based on the top-down hierarchy from regional scale to sub-catchment scale to assess the SW-GW flux. For the regional scale study, Nethravathi basin is chosen whereas for the intermediate scale, sub-catchment of Gowri-hole, a tributary of Nethravathi River is considered. In this study, river-aquifer interaction processes are simulated by using RIVER package of MODFLOW for an unconfined aquifer system. A regional groundwater flow model is built as a pre-requisite to identify the dynamic exchange occurring over the catchment area using potentiometric maps and flow budgets. In the present study, a MODFLOW-based regional groundwater model was simulated under steady-state condition for a calibration period of 2004 - 2009 and validation period of 2010 - 2011. The calibrated model was validated using the observed groundwater level data of 15 open wells measured by Department of Mines and Geology, Government of Karnataka. The simulated regional groundwater model is in good agreement with most of the wells reasonably matching the observed and computed groundwater heads. It shows that the simulation of regional groundwater model is reasonable and well suitable for the studies related to SW-GW interactions. In the present study,ii intermediate scale model for the Gowri-hole sub-catchment was calibrated for transient analysis from June 2004 - May 2010 with a daily step input. Automated Parameter Estimation analysis was carried out to get better results from the study. The calibrated model was validated from June 2010 - October 2012 for two monthly observation wells of Department of Mines and Geology and one seasonal observation well of Central Ground Water Board (CGWB). Groundwater heads gradually increase from June - August with the arrival of monsoons and decline significantly from September upto the month of May. Groundwater swelling is noticed near Well No. 3 of Bellare village in the month of October. River leakage decreases from 10 – 11 % in June to 4 – 5 % in July with the commencement of peak monsoon flows. It steadily increases from 12 – 14 % in September and continues to occur up to 41 – 42 % until the end of May. Aquifer discharge increases from 24 – 25 % in June to 34 – 35 % in July due to quick saturation during monsoon. From September, aquifer contribution into the river flow significantly decreases upto 9 – 10 % in May. The contribution of aquifer discharge into the river flow is consistent at the confluence points. Some parts of river segments are under the influence of aquifer discharge. However, majority of river segments are dominated by river leakage areas throughout the year. Consequently, Gowri hole acts as a Gaining River during monsoons due to aquifer discharge. And, it acts as Losing River due to river leakage throughout post-monsoon and summer months. SW-GW interactions are driven by aquifer parameters such as recharge rate and hydraulic conductivity. In the present study, these driving forces are calculated for the simulation of both the regional scale and sub-catchment scale groundwater flow model. During the calibration, the driving force values are adjusted until the model is simulated with a good match between computed and observed groundwater heads. The study identified that recharge rate is the driving factor influencing the SW-GW interactions at regional scale and hydraulic conductivity is the driving factor of sub-catchment scale SW-GW interactions.Item Prediction of Local scour around bridge pier using Soft Computing Techniques(National Institute of Technology Karnataka, Surathkal, 2019) B. M, Sreedhara; Manu; Mandal, S.Bridges play an essential role in the society since they enable quick access across a river or any water body. Bridges facilitate transportation of goods and people and hence play a leading role in the development of a province. The safety of the bridge is the important factor with respect to scour failure which is the leading failure factor in river bridges. Scour is the removal of sediment near or around the structure which is located in the flowing water. There are different factors which affects scour mainly on the scour depth are flow depth, discharge, velocity, sediment size, porosity, pier shape and size etc. There are two types of scour conditions on which scour is classified and studied namely, clear water and live bed scour. The scour is the complex phenomenon and there is no common or general simple method to predict the scour depth around the bridge pier. There are several researchers who studied the scour mechanism using laboratory experiments. In the present days the artificial intelligence is the focal point for several researchers. Soft computing techniques, such as, Artificial Neural Network (ANN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) have been efficiently used for modeling scour related problems. The study used the data for developing the soft computing models, is obtained from a physical model study on scour depth around bridge pier, carried out by Goswami Pankaj in 2013 in a 2-D wave flume. The input parameters, namely, sediment size (d50), velocity (U), time (t) and sediment quantity (ppm) are used to predict the scour depth of different pier shapes such as circular, rectangular, round nosed and sharp nosed pier for both clear water and live bed scour condition. The complete original data is divided into training and testing. In the study, the soft computing techniques such as ANN, SVM, ANFIS, PSO-SVM and PSO-ANN are developed. The ANN model with feed-forward backpropagation network is developed with different hidden neurons. The RBF, Linear and Polynomial kernel functions are used in the SVM model. the ANFIS model is also developed with Trapezoidal, Gbell and Triangular membership function. The evolutionary optimization technique, particle swarm optimization is used to tune the SVM and ANN parameters to improve the efficiency of models prediction.ii The performance of individual and hybrid soft computing models are compared using statistical parameters such as, Correlation Coefficient (CC), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe coefficient (NSE) and Normalized Mean Bias (NMB). Scatter plots are used to evaluate the accuracies of the models and box plots were used to analyze the spread or distribution of the data points estimated by the models. The validation of the developed models is done using the experimental values. The validation results shows that the proposed models are well correlated and in good agreement with experimental results. The hybrid models displayed a better performance compared to individual models. It is found that the hybrid PSO-SVM model is the best and efficient model in estimating the scour depth effectively around bridge pier for both live bed and clear water scour condition when compared to all the other models developed.Item Participatory Geomatics in Process based watershed development(National Institute of Technology Karnataka, Surathkal, 2013) P. G, Diwakar; Mayya, S. G.Watershed, being a hydrological unit, has its importance as a physical, biological and socio-economic entity for planning and management of natural resources. Optimal use of land and water resources in a sustainable manner results in long-term benefits to the society. Developmental activities in rural areas for resource conservation are recognized as one of the major challenges and also a complex problem to deal with. Watershed development has been in vogue for a long time and several developmental programmes have been implemented over time, but there is a need to review the conventional methods. Remote Sensing and Geographic Information System technologies are well established in these areas. Further, it is noted that community participation in the developmental process, along with monitoring and evaluation, plays a key role. Considering that about 70% of Indians live in rural areas and large proportion of these areas depend on rain fed agriculture, spread over different agro-climatic zones, it is found pertinent to explore participatory methods for natural resources management. Not much work is done on process based participatory watershed development with geomatics technology interventions. The present research focus is on developing such a model with appropriate integration of modern tools and technologies. The conventional model is analysed and an improved process based model is suggested. The proposed model is suitably improved with community role at every stage of development with an optimal blend of conventional and contemporary techniques. Participatory geomatics and information technology solutions, through innovative means, are considered for watershed development including monitoring and evalution. The proposed techniques are successfully tested through Karnataka Watershed Development programme, Karnataka State, India and the results are discussed. The outcome indicates many positive developments, that is, effective use of modern technology in planning and implementation which has resulted in improved agriculture productvity, reduced runoof, increased infiltration, self employment, improved livestock and milk yield, better socio-economic conditions and livelihood options. It is concluded that innovative means of implementing participatory watershed development have given rich dividends for natural resources development.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.