COMPARATIVE STUDY OF POWERFUL PREDICTIVE MODELING TECHNIQUES FOR MODELING MONTHLY REFERENCE EVAPOTRANSPIRATION IN VARIOUS CLIMATIC REGIONS

Estimation of reference evapotranspiration (ETo) is vibrantly required for estimating crop water requirement and budgeting irrigation scheduling. The beneficial use of water is of great importance due to shortage issues especially in developing countries like Pakistan. The Food and agricultural organization (FAO) developed a Peneman-Montieth (PM) method which can be globally considered as a standard method for estimation of ETo, but it requires numerous climatic data. Consequently, there is a need to find out the next best suitable method after PM method. The Multi-layer perceptron (MLP), Gene expression programing (GEP) and Radial basis function (RBF) were utilized to calculate ETo values. Monthly meteorological data of six different stations located in arid, semi-arid and humid regions of Pakistan covered from 1980 to 2015. Seventeen input combinations comprise of various climatic variables were developed to evaluate the impact on ETo. Of the available meteorological data, 70% was employed in training while remaining 30% used in testing process. The yielded values of the developed models were compared with the ETo estimated by PM method. The outcome of the study was also applied on some other climatic regions located in USA, New Zealand and China for numerous duration only three climatic parameters, namely, maximum temperature, mean relative humidity and wind velocity had a large positive effect on increasing the accuracy of estimating ETo. By comparing the eight performing indices, MLP among all the powerful predictive modeling techniques can also be considered as the superior alternative to the conventional methods in estimation of ETo.

Automated summary

The study suggests that in estimating crop water requirements and irrigation scheduling, MLP stands out as a superior alternative to traditional methods, enhancing the accuracy of ETo estimation.