ARRSES method for predicting quantities supplied from modern agricultural irrigation systems

Abstract

There is an urgent need for accurate, reliable and administratively reliable forecasting
systems. Prediction is one of the main keys to decision-making management because
future events carry a great deal of uncertainty. Today, methods, methods and models for
prediction have varied based on the availability of data. Whether or not, the nature of that
data, the type of prediction required, and the amount of expected accuracy ... etc.
Studies and research that address the subject of forecasting in the agricultural sector are
distinguished by their scarcity and simplicity, which are traditional estimates that lack the
basis and the solid scientific basis in building them, and the aim of this research is to
predict the numbers that will be prepared by the General Company for Agricultural
Supplies from modern agricultural irrigation systems in Iraq in 2019 by using the
statistical method called ARRSES. The most important characteristic of the ARRSES
method is that it gives the most recent observations (values), giving it more weight
(weighting) than previous observations because the newest observations carry more
realistic information about the situation under study, and this method can change the value
of α (a constant whose value is between zero and one) in a sequential manner whenever
the need arises (i.e. whenever the α value appears to be inconsistent with the nature of the
available data).