Rainfall Data Calculation Using
Artificial Neural Networks And
Adaptive Neuro-fuzzy Inference Systems
Author(s)
L. Mpallas, C. Tzimopoulos & C. Evangelidis
Abstract
Missing rainfall data is a frequent problem in every hydrological or water
management study. Conventional methods are usually based on statistics and the
missing rainfall data are estimated using neighbouring stations. The same
procedure is implemented here using two models based on artificial intelligence
techniques. A fuzzy logic and an Artificial Neural Network model are used to
predict rainfall heights of two meteorological stations. A number of model
parameters were modified during the calibration, in order to achieve the best
performance. The comparison between observed and estimated values showed
that both models perform satisfactorily in the prediction of rainfall data.
Keywords: fuzzy logic, rainfall, neural networks, hydrology.
1 Artificial intelligence
Artificial intelligence includes a number of methods that mimic human
reasoning. These methods can be modelled and applied by a computer. The most
common ones are Artificial Neural Networks (ANN), Genetic Algorithms (GA)
and fuzzy logic theory.
Artificial intelligence is an alternative and also effective method in studying
and modelling hydrological systems. They capture their ambiguous and
inaccurate nature, even when non-linearities exist. These models are extremely
effective when the knowledge of the system is insufficient or when the
underlying procedures are not fully understood.
Keywords
fuzzy logic, rainfall, neural networks, hydrology
Related Book
Other papers in this volume
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