Abstract-Suspended

Water-resource investigations should embrace and deal with effective reservoir water storage, which can be employed for water supply, irrigation, providing flood and drought control, hydropower and energy security of local generation. Dam reservoirs are used to provide storage for water and are important elements in planning and managing water resources. If the dam reservoir level is estimated correctly, the most suitable dam operation performance can be obtained. Reservoir level parameters are inflow to reservoir, water storage in reservoir, water release from reservoir, evaporation, soil moisture and infiltration. They represent uncertainties and must be considered in water resource operation. Reservoir water level is also important in the analysis and design of several water resource projects such as dam construction, irrigation needs and flood control. During a flood, the opening of the dam weir passage should be sufficient to ensure that reservoir

In order to determine the use, protection and economic life of water resources; it is important to make estimations about rainfall-runoff values. However, it is quite complicated to estimate rainfall-runoff. For this reason, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods, which are widely used today for complex hydrological problems, are preferred for the rainfall-runoff model. For model creation, the hydrological and seasonal data from the United States Muskegon basin are used. Estimation study was done with ANN and MLR methods using 1396 daily rainfall, temperature and rainfall data belonging to the region. According to the model results, it is seen that the ANN method has results with low error and high determination in the rainfall runoff model. ANN method can be used as an alternative way to classical methods in rainfall-runoff predictions.

Rainfall-Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall-Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall-Runoff prediction.

Estimation of Ground Water Level (GWL) is important in the determination of the sustainable use of water resources and Ground Water resources. Groundwater level fluctuations were investigated using the variable of groundwater level, precipitation, temperature. In the present study, GWL estimation studies were conducted via Neuro-Fuzzy (NF), Support Vector Regression with radial basis functions (SVR-RBF) and Support Vector Regression with poly kernel (SVR-PK) models. The daily data of the precipitation, temperature and groundwater level are used which is taken from Minnesota, United States of America. The results were compared with NF and SVR methods. According to this comparison, it was observed that the NF and SVR models gave similar results for observation.