Data Pre-Processing and Evaluating the Performance of Several Data
  Mining Methods for Predicting Irrigation Water Requirement
  Recent drought and population growth are planting unprecedented demand for
the use of available limited water resources. Irrigated agriculture is one of
the major consumers of freshwater. A large amount of water in irrigated
agriculture is wasted due to poor water management practices. To improve water
management in irrigated areas, models for estimation of future water
requirements are needed. Developing a model for forecasting irrigation water
demand can improve water management practices and maximise water productivity.
Data mining can be used effectively to build such models.
  In this study, we prepare a dataset containing information on suitable
attributes for forecasting irrigation water demand. The data is obtained from
three different sources namely meteorological data, remote sensing images and
water delivery statements. In order to make the prepared dataset useful for
demand forecasting and pattern extraction, we pre-process the dataset using a
novel approach based on a combination of irrigation and data mining knowledge.
We then apply and compare the effectiveness of different data mining methods
namely decision tree (DT), artificial neural networks (ANNs), systematically
developed forest (SysFor) for multiple trees, support vector machine (SVM),
logistic regression, and the traditional Evapotranspiration (ETc) methods and
evaluate the performance of these models to predict irrigation water demand.
Our experimental results indicate the usefulness of data pre-processing and the
effectiveness of different classifiers. Among the six methods we used, SysFor
produces the best prediction with 97.5% accuracy followed by a decision tree
with 96% and ANN with 95% respectively by closely matching the predictions with
actual water usage. Therefore, we recommend using SysFor and DT models for
irrigation water demand forecasting.
