Evapotranspiration is an important component of the
hydrologic cycle, and the accurate prediction of this parameter is
very important for many water resources applications. Thus, the
aim of this study is prediction of monthly reference
evapotranspiration using Artificial Neural Networks (ANNs) and
fuzzy inference system (FIS).
Suspension system is considered one of the most important components of modern
automobiles as it is the responsible for the vehicle’s stability, balance and safety. The
presence of robust controller is very necessary in order to ensure full interac
tion between
suspension components and making accurate decisions at the right time. This paper
proposes to design an Extended Adaptive Neuro Fuzzy Inference System (EANFIS)
controller for suspension system in quarter car model. The proposed controller is used as
decision maker In order to contribute in absorbing shocks caused by bumpy roads, and to
prevent vibrations from reaching the cockpit. Furthermore, it provides stability and
coherence required to reduce the discomfort felt by passengers, which arises from road
roughness, which in turn, improve the road handling. The MATLAB Simulink is used to
simulate the proposed controller with the controlled model and to display the responses of
the controlled model under different types of disturbance. In addition, a comparison
between EANFIS controller, Fuzzy controller and open loop model (passive suspension)
was done with different types of disturbance on order to evaluate the performance of the
proposed model. Controller has shown excelled performance in terms of reducing
displacements, velocity and acceleration.
Accurate estimating and predicting of hydrological phenomena plays an influential role in the development and management of water resources, preparing of future plans according to different scenarios of climate changes. Evapotranspiration is one of t
he major meteorological components of the hydrologic cycle and from the most complex of them, and the accurate prediction of this parameter is very important for many water resources applications.
So, this research goals to prediction of monthly reference evapotranspiration (ET0) at Homs meteostation, in the middle of Syrian Arab Republic, using Artificial Neural Networks (ANNs), and Fuzzy Inference System (FIS), depending on available climatic data, and comparision between the results of these models.
The used data contained 347 monthly values of Air Temperature (T), Relative Humidity (RH), Wind Speed (WS) and Sunshine Hours (SS) (from October 1974 to December 2004). The monthly reference evapotranspiration data were estimated by the Penman Monteith method, which is the proposed method by Food and Agriculture Organization of the United Nations (FAO) as the standard method for the estimation of ET0, and used as outputs of the models.
The results of this study showed that feed forward back propagation Artificial Neural Networks (FFBP-ANNs) pridected successfully the monthly ET0 using climatic data, with low values of root mean square errors (RMSE), and high values of correlation coefficients (R), and showed that the using of the monthly index as an additional input, improves the accurate of prediction of the artificial neural networks models.
Also, the results showed good ability of Fuzzy Inference Models (FIS) in predicting of monthly reference evapotranspiration. Sunshine hours are the most influential single parameter for ET0 prediction (R= 97.71%, RMSE = 18.08 mm/month) during the test period, sunshine hours and wind speed are the most influential optimal combination of two parameters
(R= 98.55%, RMSE = 12.49 mm/month) during the test period.
The results showed high reliability for each of the artificial neural networks and fuzzy inference system with a little preference for artificial neural networks which can add the monthly index in the input layer, and there for improve the presicion of predictions.
This study recommends the using of artificial intelligence techniques in modeling of complex and nonlinear phenomena which related of water resources.
