Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)

Abstract

Evaporation is a major component of the hydrological cycle, it is an important aspect of water resource engineering and management, and in estimating the water budget of irrigation schemes.The current work presents the application of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches for modeling daily pan evaporation using daily climatic parameters. The neuro-fuzzy and neural network models are trained and tested using the data of three weather stations from different geographical positions in the U.S. State of Illinois. Daily meteorological variables such as air temperature, solar radiation, wind speed, relative humidity, surface soil temperature and total rainfall for three years (August 2005 to September 2008) were used for training and testing the employed models. Statistic parameters such as the coefficient of determination {R^), the root mean squared error (RMSE), the variance accounted for (VAF), the adjusted coefficient of efficiency (f i) and the adjusted index of agreement (di) are used to evaluate the performance of the applied techniques. The results obtained show the feasibility of the ANFis and ANN evaporation modeling from the available climatic parameters, especially when limited climatic parameters are used.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

“…Pan evaporation (Ep) has been widely used as an index of lake and reservoir evaporation, potential or reference crop evapotranspiration and irrigation (Shiri et al, 2011), which plays important roles in informing water resources distribution and irrigation system design. There are many climatic factors influencing the rates of Ep, including solar radiation (Rg), air temperature (Ta), relative humidity (RH) and wind speed (Ws).…”

Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine

“…For instance, the artificial neural network (ANN) has been applied in creating a model of evapotranspiration process (Sudheer et al, 2003;Kisi, 2007;Khoob, 2008;Kumar et al, 2011;Landeras et al, 2008;Cobaner, 2011). The adaptive neuro-fuzzy inference system (ANFIS) has been developed and applied to estimate ET 0 (Shiri et al, 2011;Kisi and Zounemat-Kermani, 2014;Citakoglu et al, 2014;Petkovic et al, 2015). Genetic programming (GP) is used for mathematical formulation of the ET 0 (Kisi and Cengiz, 2013;Traore and Guven, 2012;Shiri et al, 2014a).…”

Estimation of natural water body’s evaporation based on Class A pan measurements in comparison to reference evapotranspiration

“…E p has also been applied as an index of lake and reservoir E (Wang et al, 2017;Kim et al, 2015;Allen et al, 1998) beyond traditional E p uses in water budget estimation, plant-weather interactions, etc. Spatial and temporal limitations of pan application due to instrumental and practical issues were also integrated (Martí et al, 2015;Shiri et al, 2011). Several empirical methods based on local variables, in many cases various meteorological drivers, have been developed to estimate E p in different climate conditions.…”