Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques |
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| Authors: | A Moghaddamnia M Ghafari Gousheh J Piri S Amin D Han |
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| Institution: | 1. Department of Watershed and Range Management, Faculty of Natural Resources, University of Zabol, Iran;2. Department of Range and Watershed Management, Faculty of Natural Resources, University of Zabol, Iran;3. Management of Agriculture, Zabol, Iran;4. Department of Water Engineering, Faculty of Agriculture, University of Shiraz, Iran;5. Department of Civil Engineering, Faculty of Engineering, University of Bristol, UK |
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| Abstract: | Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical formulas available, their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. This paper explores evaporation estimation methods based on artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) techniques. It has been found that ANN and ANFIS techniques have much better performances than the empirical formulas (for the test data set, ANN R2 = 0.97, ANFIS R2 = 0.92 and Marciano R2 = 0.54). Between ANN and ANFIS, ANN model is slightly better albeit the difference is small. Although ANN and ANFIS techniques seem to be powerful, their data input selection process is quite complicated. In this research, the Gamma test (GT) has been used to tackle the problem of the best input data combination and how many data points should be used in the model calibration. More studies are needed to gain wider experience about this data selection tool and how it could be used in assessing the validation data. |
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| Keywords: | Evaporation Artificial neural networks Adaptive neuro-fuzzy inference system Gamma test Input data selection |
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