CAMEL: AN INTELLIGENT COMPUTATIONAL MODEL FOR AGRO-METEOROLOGICAL DATA
Weather plays an important role in agriculture.This calls for reliable weather information, which in turn helps farmers make management decisions about their crops.In this paper, we propose an intelligent Computational model for Agro-MEteoroLogical data (CAMEL).The model serves three purposes.First, it effectively captures important information about large amounts of data collected from various weather stations distributed in a wide geographic expanse.Second, the proposed model learns from historical data and predicts future trends.This helps us obtain accurate weather forecasts.Third, through the prediction of weather trends, CAMEL gives us a better understanding of agro-meteorological data.When we compare the predicted results with the observed data, any significant difference between them may be an indication of equipment malfunction or other problems.In this way, CAMEL helps us detect abnormal data and facilitates in guarding against potential sources of error.Consequently, well-functioning equipment and accurate weather data help farmers make wise crop management decisions.Experimental results on real-life datasets show the effectiveness of our proposed intelligent computational model for agro-meteorological data.
Intelligent computational model Knowledge discovery and data mining Machine learning Neural network Prediction Outlier detection Data quality control Quality assurance Weather data
CARSON K.-S.LEUNG MARK A.F.MATEO ANDREW J.NADLER
Department of Computer Science, The University of Manitoba, Canada Manitoba Agriculture, Food and Rural Initiatives (MAFRI), Canada
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
1960-1965
2007-08-19（万方平台首次上网日期，不代表论文的发表时间）