Data Pre-Processing and Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement
School of Environmental Sciences, Charles Sturt University（查尔斯斯特大学环境科学学院）；School of Computing and Mathematics, Charles Sturt University（查尔斯斯特大学计算与数学学院）；International Water Management Institute (IWMI)（国际水资源管理研究所）
Mahmood A. Khan, Md Zahidul Islam, Mohsin Hafeez
备注：
This 13-page paper is a slightly modified version of our original conference paper published in the 10th Australasian Data Mining Conference 2012. We then submitted the paper to the Journal of Research and Practice in IT (JRPIT) as an invited paper. However, despite the acceptance for publication the paper was never published by JRPIT since the journal discontinued after it had accepted our paper
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.
Trajectory Data Mining and Trip Travel Time Prediction on Specific Roads
Data Science Consultant（数据科学顾问）；Datamatics Technologies（Datamatics技术公司）；Department of CS&IT Women University of AJ&K（CS&IT系妇女大学AJ&K分校）；Classified institute（保密机构）；Department of Computer Science（计算机科学系）；Kohsar University（Kohsar大学）；Disruptive Innovation Laboratory（颠覆性创新实验室）；PIEAS
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being
University of Notre Dame（圣母大学）；University of Colorado, Boulder（科罗拉多大学博尔德分校）；University of California, Irvine（加州大学欧文分校）；Dartmouth College（达特茅斯学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Washington（华盛顿大学）
Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods
Tallinn University（塔林大学）；University of Jyväskylä（于韦斯屈莱大学）；Gangneung-Wonju National University（江陵原州国立大学）；Czech Technical University（捷克技术大学）；University of Central Florida（中佛罗里达大学）
Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
Kashani Comprehensive Epilepsy Center, Kashani Hospital（卡沙尼综合癫痫中心，卡沙尼医院）；Isfahan University of Medical Sciences（伊斯法罕医科大学）；Staffordshire University（斯塔福德郡大学）
