No abstract

Reservoir level modeling is important for the operation of dam reservoir, design of hydraulic structures, determining pollution in reservoir and the safety of dam. In this study, daily reservoir levels for Millers Ferry Dam on the AlabamaRiver in USA were predicted using artificial neural networks (ANN) IntroductionReservoir level is a complex index of natural water exchange within their watersheds. Longterm level fluctuations in natural (unregulated) lakes and large dam reservoirs also reflect climate change occurring in the region. Dams are also the most important fresh water sources and their construction is very expensive. Reservoir level fluctuations are also important not only in the planning, designing, and operating the fresh water reservoir made for any purpose (i.e. domestic, industrial, hydropower, and irrigation water supply as well as flood control, navigation, and water quality and quantity improvement) but also in all hydraulic structures. Therefore, they must be well planned and operated for the maximum return benefits. Level measurements or their future equally likely replicas obtained through a estimation model are a direct way of obtaining lake management decision variable. Although it is possible to identify sophisticated models taking into consideration the hydrological and hydro meteorological parameters such as the precipitation, runoff, humidity and temperature, it is economically preferred if a model that simulates the level variations on the basis of past level records is at the hand of the decision maker whether he/she be administrator, local authority or a technical operator (Sen et al. [1]).Reservoir water level forecasting at various time intervals using the records of past time series is an important issue in water resources planning (engineering, etc.). Variations in reservoir level are complex outcomes of many environmental factors, such as precipitations, direct and indirect runoffs from neighbor catchments, evaporation from free water body, air and water temperature, and interactions between the reservoir and the low lying aquifers. Although it is possible to identify sophisticated models taking into consideration the aforementioned parameters, it is preferable that a model which simulates lake-level variations based on previously recorded lake levels be available for research as well as practical purposes.Recently, the use of artificial intelligence (AI) methods such as artificial neural networks (ANNs), adaptive neuro-fuzzy has been accepted as an appropriate tool for modeling complex nonlinear phenomena in hydrology and water resources systems, leading to widening of their applications. In this context,

Abstract-Suspended

Groundwater Level Predıctıon Usıng Artıfıcıal Neural Network and M5 Tree Models. Most of the fresh water resources in our world consist of underground water reserves. Estimation of fluctuations of groundwater level (GWL) is very important in the management of water resources. In this study, groundwater level (GWL) was investigated using artificial neural networks (ANN), M5tree (M5T) approaches in Reyhanlı region in Turkey. Total 196 data from 2000-2015 taken from 1 observation station belonging to Reyhanlı sub-basin located in Asi basin were used in the study. Using the monthly average precipatation and temperature, the change in GWL is modeled by artificial neural networks (ANN), M5tree (M5T) approaches. The results showed that (ANN) and M5tree (M5T) models were found to be very close to each other.

No abstract

Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (P min ) and maximum (P max ) power generation values as well as the total power generation (P sum ) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, P min , P max , and P sum , were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models.Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phase of study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.

No abstract

No abstract