APPLICATION OF DATA MINING TECHNIQUES IN THE PREDICTION OF CLIMATE EFFECT ON AGRICULTURE. A RESEARCH PROJECT MATERIAL ON COMPUTER SCIENCE EDUCATION
The purpose of this study is to examine the application of data mining techniques in the prediction of climate effect on agriculture with discussion on different data mining methods which are helpful in building a predictive data mining model.
The Hybrid Knowledge Discovery Process for Data Mining is followed to build the predictive model that analyzes and predicts the agricultural output. This methodology was developed, by adopting the Cross-Industry Standard Processes for Data Mining (CRISP-DM) model to the needs of academic research community. The work is based on finding suitable data sets as well as best predictive model that helps in achieving high accuracy and generality for Agricultural output for the selected climatic indicators (Rainfall, Temperature and Humidity). For solving this problem, different data mining classification techniques (Eight WEKA classifiers: Gaussian Processes, Linear Regression, Multilayer Percepton, SMOreg, Decision Table, M5Rules, M5P and REPTree) were evaluated on different data sets.
The experimental results obtained from this study shows; the Decision Table Classifier has the highest optimal accuracy score with maximum optimal Correlation Coefficient Percentage (CCP) of 97.8%, minimum optimal Root Mean Square Percentage Error (RMSPE) of 3.9% and an optimal Time of 0.02 seconds to build the Agricultural Output Predictive model.
Finally, by extending WEKA software source code, an application (predictive-model-prototype) which is termed as “Agricultural Output Predictive System” with a user-friendly GUI is developed and deployed for the usage of domain experts (end users). Therefore, the results obtained from this research indicate that data mining classification models are very useful in predicting agricultural outcomes for the effective and efficient utilization of available climatic data to support experts and farmers in making strategic planning as well as proactive and knowledge-driven decisions.