APPLICATION OF DATA MINING TECHNIQUES IN THE PREDICTION OF CLIMATE EFFECT ON AGRICULTURE
ABSTRACT
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.
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND TO THE STUDY
Prediction of climate effect plays an essential role for agriculture and other industrial sector. A vast proportion of agricultural activities are convincingly affected by climate conditions. From the short-term perspective, the temperature and precipitation are essential condition for crop growth and yield in agriculture. In agricultural practices, every crop has its own minimum, optimal, maximum temperature for growing. The crop stops growing when the temperature goes below the minimum temperature. The crop growth increased as the temperature goes up from the minimum to the maximum temperature. However the crop growth decreases as the temperature goes beyond its optimal temperature to the maximum temperature. The crop growth stops again when the temperature reaches its maximum temperature. Warmer temperature may favor some crops to grow more quickly and increase their yields, but it could also reduce growth and yields for other kinds of crops. So accurate prediction of future climate effect and weather condition could help farmers select the proper crops in order to increase growth and yield as well as economic incomes. The fast growth of crops such as grains may reduce the amount of time that seeds need to grow and mature (Semenov and Porter, 2005). The crop growth not only depends on the temperature, but also on soil water and nutrient elements such as nitrogen, phosphorus and potassium which are all connected to the climatic conditions. The soil nutrients are absorbed by crops with soil water adsorption. The proper soil moisture is strongly related to the precipitation.
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APPLICATION OF DATA MINING TECHNIQUES IN THE PREDICTION OF CLIMATE EFFECT ON AGRICULTURE
