Comparison of Nearest Neighbor (ibk), Regression by Discretization and Isotonic Regression Classification Algorithms for Precipitation Classes Prediction

Published ArticleSelection of classifier for use in prediction is a challenge. To select the best classifier comparisons can be made on various aspects of the classifiers. The key objective of this paper was to compare performance of nearest neighbor (ibk), regression by discretization and isotonic regression classifiers for predicting predefined precipitation classes over Voi, Kenya. We sought to train, test and evaluate the performance of nearest neighbor (ibk), regression by discretization and isotonic regression classification algorithms in predicting precipitation classes. A period of 1979 to 2008 daily Kenya Meteorological Department historical dataset on minimum/maximum temperatures and precipitations for Voi station was obtained. Knowledge discovery and data mining method was applied. A preprocessing module was designed to produce training and testing sets for use with classifiers. Isotonic Regression, K-nearest neighbours classifier, and RegressionByDiscretization classifiers were used for training training and testing of the data sets. The error of the predicted values, root relative squared error and the time taken to train/build each classifier model were computed. Each classifier predicted output classes 12 months in advance. Classifiers performances were compared in terms of error of the predicted values, root relative squared error and the time taken to train/build each classifier model. The predicted output classes were also compared to actual year classes. Classifier performances to actual precipitation classes were compared. The study revealed that the nearest neighbor classifier is a suitable for training rainfall data for precipitation classes prediction

The Vegetation Outlook (VegOut): A New Tool for Providing Outlooks of General Vegetation Conditions Using Data Mining Techniques

The integration of climate, satellite, ocean, and biophysical data holds considerable potential for enhancing our drought monitoring and prediction capabilities beyond the tools that currently exist. Improvements in meteorological observations and prediction methods, increased accuracy of seasonal forecasts using oceanic indicators, and advancements in satellite-based remote sensing have greatly enhanced our capability to monitor vegetation conditions and develop better drought early warning and knowledge-based decision support systems. In this paper, a new prediction tool called the Vegetation Outlook (VegOut) is presented. The VegOut integrates climate, oceanic, and satellite-based vegetation indicators and utilizes a regression tree data mining technique to identify historical patterns between drought intensity and vegetation conditions and predict future vegetation conditions based on these patterns at multiple time steps (2-, 4-, and 6-week outlooks). Cross-validation (withholding years) revealed that the seasonal VegOut models had relatively high prediction accuracy. Correlation coefficient (R 2) values ranged from 0.94 to 0.98 for 2-week, 0.86 to 0.96 for 4-week, and 0.79 to 0.94 for 6-week predictions. The spatial patterns of predicted vegetation conditions also had relatively strong agreement with the observed patterns from satellite at each of the time steps evaluated. 1

Critical appraisal of different drought indices of drought predection & their application in kbk districts of odisha

Mapping of the extreme events (droughts) is one of the adaptation strategies to consequences of increasing climatic inconsistency and climate alterations. Drought is one of the short-term extreme events. There is no operational practice to forecast the drought. One of the suggestions is to update mapping of drought prone areas for developmental planning. Drought indices play a significant role in drought mitigation. Many scientists have worked on different statistical analysis in drought and other climatological hazards. Many researchers have studied droughts individually for different sub-divisions or for India. Very few workers have studied district wise probabilities over large scale. In the present study, district wise drought probabilities over KBK (Kalahandi-Balangir-Koraput) districts of Odisha, which are seriously prone to droughts, has been established using meteorological, hydrological and remote sensing based agricultural droughts indices. The meteorological droughts indices are: percentage departure, percentage to normal, percentile, Standard Precipitation index (SPI), Reclamation Drought Index (RDI), Effective drought index (EDI), and Aridity Index (AI). The hydrological drought indices are: Streamflow drought index (SDI), Surface water supply index and proposed drought severity index (PDSI). The satellite data based agricultural drought indices was Normalized Difference Vegetation Index (NDVI). Mapping for moderate and severe drought probabilities for KBK districts has been done and regions belonging different class intervals of probabilities of drought have been demarcated. Such type of information would be a good tool for planning purposes and for input in modelling. Moreover, the present work discusses (a) composite drought indices with the combinations of meteorological, hydrological and satellite data based agricultural drought index, and (b) development of a proposed hydrological drought index

University of Nebraska-Lincoln Agricultural Research Division 122nd Annual Report July 1, 2007 to June 30, 2008

Our Mission ..... 4 Foreword..... 5 Faculty Awards and Recognitions.... 6 Graduate Student Awards and Recognitions...10 Undergraduate Honors Student Research Program...14 Variety and Germplasm Releases....15 Patents.....17 Administration..... 18 Administrative Personnel.... 18 Organizational Chart....19 Administrative Units....20 IANR Research Facilities.... 21 Faculty..... 22 Agricultural/Natural Resources Units... 23 Education and Human Sciences Departments...33 Off-Campus Research Centers....34 Interdisciplinary Activities....35 Visiting Scientists/Research Associates....36 Research Projects..... 43 Agricultural/Natural Resources Units... 43 Education and Human Sciences Departments...48 Off-Campus Research Centers....49 Interdisciplinary Activities ....50 Publications.....51 Agricultural/Natural Resources Units... 57 Education and Human Sciences Departments...77 Off-Campus Research Centers....80 Research Expenditures....8