Enhancing monthly lake levels forecasting using heuristic regression techniques with periodicity data component: application of Lake Michigan
نویسندگان
چکیده
This study investigates the accuracy of three different techniques with periodicity component for estimating monthly lake levels. The are multivariate adaptive regression splines (MARS), least-square support vector (LSSVR), and M5 model tree (M5-tree). Data from Lake Michigan, located in USA, is used analysis. In first stage modeling, were applied to forecast level fluctuations up 8 months ahead time intervals. second stage, influence was (month number year, e.g., 1, 2, 3, …12) as an external subset modeling root-mean-square error, mean absolute coefficient determination evaluating models. both stages, comparison results indicate that MARS generally outperforms LSSVR M5-tree. Further, it has been discovered including input models improves their projecting
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ژورنال
عنوان ژورنال: Theoretical and Applied Climatology
سال: 2022
ISSN: ['1434-4483', '0177-798X']
DOI: https://doi.org/10.1007/s00704-022-03982-0