C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Prediction of subsidence due to underground mining by artificial neural networks
Computers & Geosciences
A comparison of two data mining techniques to predict abnormal stock market returns
Intelligent Data Analysis
EXPLORE: a novel decision tree classification algorithm
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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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 fresh water. Huge 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 Irrigation water demand forecasting based on historical data is critical to effectively improve the water management practices and maximise water productivity. Data mining can be used effectively to build such models. Data mining is capable of extracting and interpreting the hidden patterns from a large amount of hydrological data. In recent years, use of data mining has become more common in hydrological modelling. In this paper, we compare the effectiveness of six 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 using pre-processed dataset. The pre-processed dataset we use in this study and SysFor were never used before to compare with any other classification techniques. Our experimental result indicates SysFor produces the best prediction with 97.5% accuracy followed by decision tree with 96% and ANN with 95% respectively by closely matching the predictions for water demand with actual water usage. Therefore, we recommend using SysFor and DT models for irrigation water demand forecasting.