Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, forecasting the amount and timing of snowmelt runoff especially in the Euphrates Basin, where large dams are located, is an important task in order to use the water resources of the country in an optimum manner. The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km2 on the headwaters of Euphrates River for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in Upper Euphrates Basin operating in real-time. Since ground based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of MODIS optical satellite. Automatic model parameter estimation methods, GML and SCE_UA, are utilized to calibrate the HBV model parameters with a multi-objective criteria using runoff as well as snow covered area to ensure the internal validity of the model and to generate a Pareto front. Model simulations show that the choice of study years and timing of satellite images affect the results and further suggest that more study catchments and years should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from numerical weather prediction models of ECMWF and MM5 for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting using numerical weather prediction models in order to prevent or at least take precautions before flooding ahead of time.

The Master's thesis deals with the simulations kinematics and dynamics of experimental four-wheeled vehicle with all-wheel steering and all-wheel drive. Suggestion of vehicle stability systems ABS/ASR for traction control is included. There are several dynamics models with their comparison. The estimation of important vehicle parameters is implemented. Based on knowledge the simple vehicle stability system ABS/ASR is created.

This thesis aims to solve the mathematical inverse problem of characterizing sea waves based on the responses obtained from a marine vessel sailing under certain sea conditions. By researching this problem the thesis contributes to the marine industry by improving products that are using ocean behavior for controlling ship's dynamics. Knowledge about the current state of the sea, such as the wave frequency and height, is important for navigation, control, and for the safety of a vessel. This information can be retrieved from specialized weather reports. However, such information is not at all time possible to obtain during a voyage, and if so usually comes with a certain delay. Therefore this thesis seeks solutions that can estimate on-line the waves' state using methods in the field of Artificial Intelligence. The specific investigation methods are Transfer Functions augmented with Genetic Algorithm, Artificial Neural Networks and Case-Based Reasoning. These methods have been configured and validated using the n-fold cross validation method. All the methods have been tested with an actual implementation. The algorithms have been trained with data acquired from a marine simulation program developed in Simulink. The methods have also been trained and tested using monitored data acquired from an actual ship sailing on the Baltic Sea as well as wave data obtained from a buoy located nearby the vessel's route. The proposed methods have been compared with state-of-the art reports in order evaluate the novelty of the research and its potential applications in industry. The results in this thesis show that the proposed methods can in fact be used for solving the inverse problem. It was also found that among the investigated methods it is the Transfer Function augmented with Genetic Algorithm which yields best results. This Master Thesis is conducted under the Master of Engineering Program in Robotics at Mälardalens högskola in Västerås, Sweden. The thesis was proposed by Q-TAGG R&D AB in Västerås, Sweden, a company which specializes in marine vessel dynamics research.