Journal article
Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach
IEEE transactions on geoscience and remote sensing, Vol.51(4), pp.2337-2342
2013
DOI: 10.1109/TGRS.2012.2210429
Abstract
Rainfall affects local water quantity and quality. A data-mining approach is applied to predict rainfall in a watershed basin at Oxford, Iowa, based on radar reflectivity and tipping-bucket (TB) data. Five data-mining algorithms, neural network, random forest, classification and regression tree, support vector machine, and k -nearest neighbor, are employed to build prediction models. The algorithm offering the highest accuracy is selected for further study. Model I is the baseline model constructed from radar data covering Oxford. Model II predicts rainfall from radar and TB data collected at Oxford. Model III is constructed from the radar and TB data collected at South Amana (16 km west of Oxford) and Iowa City (25 km east of Oxford). The computation results indicate that the three models offer similar accuracy when predicting rainfall at current time. Model II performs better than the other two models when predicting rainfall at future time horizons.
Details
- Title: Subtitle
- Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach
- Creators
- Andrew KUSIAK - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA 52242-1527, United StatesXiupeng Wei - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA 52242-1527, United StatesAnoop Prakash VERMA - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA 52242-1527, United StatesEvan ROZ - Intelligent Systems Laboratory, The University of Iowa, Iowa City, IA 52242-1527, United States
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on geoscience and remote sensing, Vol.51(4), pp.2337-2342
- Publisher
- Institute of Electrical and Electronics Engineers; New York, NY
- DOI
- 10.1109/TGRS.2012.2210429
- ISSN
- 0196-2892
- eISSN
- 1558-0644
- Language
- English
- Date published
- 2013
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9984064561802771
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