C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining with decision trees and decision rules
Future Generation Computer Systems - Special double issue on data mining
Nonlinear regression: a hybrid model
Computers and Operations Research
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Machine Learning
Experience, generations, and limits in machine learning
Theoretical Computer Science - Super-recursive algorithms and hypercomputation
Expert Systems with Applications: An International Journal
Applied Soft Computing
Expert Systems with Applications: An International Journal
Fuzzy sets in machine learning and data mining
Applied Soft Computing
| Hi-index | 0.00 |
This study presented various soft computing techniques for forecasting the hourly precipitations during tropical cyclones. The purpose of the current study is to present a concise and synthesized documentation of the current level of skill of various models at precipitation forecasts. The techniques involve artificial neural networks (ANN) comprising the multilayer perceptron (MLP) with five training methods (denoted as ANN-1, ANN-2, ANN-3, ANN-4, and ANN-5), and decision trees including classification and regression tree (CART), Chi-squared automatic interaction detector (CHAID), and exhaustive CHAID (E-CHAID). The developed models were applied to the Shihmen Reservoir Watershed in Taiwan. The traditional statistical models including multiple linear regressions (MLR), and climatology average model (CLIM) were selected as the benchmarks and compared with these machine learning. A total of 157 typhoons affecting the watershed were collected. The measures used include numerical statistics and categorical statistics. The RMSE criterion was employed to assess the suitable scenario, while the categorical scores, bias, POD, FAR, HK, and ETS were based on the rain contingency table. Consequently, this study found that ANN and decision trees provide better prediction compared to traditional statistical models according to the various average skill scores.