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Evaporation modeling with artificial neural network: A review

Parameshwar Sidramappa Shirgure

Abstract


Evaporation from the open pan as well as surface is a complex phenomenon of the hydrological cycle and influenced by many meteorological parameters, such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours. Measurement of evaporation with accuracy is and continuous is a difficult operation. In such situations, it becomes an imperative to use neural network models that can estimate evaporation from available climatic data and may give more accurate results than the measured pan evaporation. In this regard, a number of models for predicting the pan evaporation have been developed by several investigators for different locations of India and abroad. Most of the current models for predicting evaporation use the principles of the deterministically based combined energy balance – vapor transfer approach or empirical relationships based on climatological variables. This resulted in relationships that were often subjected to rigorous local calibrations and therefore proved to have limited global validity. Due to these limitations the conventionally applied regression modeling techniques need to be further refined to achieve improved performance by adopting new and advanced technique like neural networks. Evaporation process is complex and needs non-linear modeling and hence, can be modeled through Artificial Neural Networks (ANN). Large number of researchers have been established the applicability of artificial neural networks (ANNs) to the problems in agricultural, hydrological, meteorological and environmental fields. The review related to evaporation modeling using neural networks is discussed here in brief.


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