Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach

摘要

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.