Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

The Learning of Multivariate Adaptive Regression Splines (MARS) Model in Rainfall-Runoff Processes at Pahang River Catchment

The Learning of Multivariate Adaptive Regression Splines (MARS) Model in Rainfall-Runoff... AbstractRecently, a novel data mining technique, Multivariate Adaptive Regression Splines (MARS) has begun attracted attention from several hydrological researchers because their application is relatively new in modelling hydrological processes. The power of this approach has been proven in variety learning problems such as financial analysis, species distributions modelling, and doweled pavement performance modelling. Therefore, the objective of this paper is to investigate the performance of MARS model in capture the rainfall-runoff processes at river catchment of Malaysia. Pahang River has been selected as area of study. 30-years data set of daily rainfall and runoff at upstream tributaries of Pahang River were used to developed and validate the capability of MARS model in flood prediction. The effect of different length of record data to performance of MARS model was also examined by arranged the data into 5-years data set, 10 years data set, 20 years data set, and 30 years data set. All these data sets used 1-year data of 2003 for validation process while the others were applied for calibration. Simulation results showed that MARS model was able to learn the rainfall-runoff processes in Pahang River catchment and the model performance improved due to the longer period of data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Valahia University of Targoviste, Geographical Series de Gruyter

The Learning of Multivariate Adaptive Regression Splines (MARS) Model in Rainfall-Runoff Processes at Pahang River Catchment

Loading next page...
 
/lp/de-gruyter/the-learning-of-multivariate-adaptive-regression-splines-mars-model-in-CNfpI614xk
Publisher
de Gruyter
Copyright
© 2019 D.A. Halid et al., published by Sciendo
eISSN
2393-1493
DOI
10.2478/avutgs-2018-0018
Publisher site
See Article on Publisher Site

Abstract

AbstractRecently, a novel data mining technique, Multivariate Adaptive Regression Splines (MARS) has begun attracted attention from several hydrological researchers because their application is relatively new in modelling hydrological processes. The power of this approach has been proven in variety learning problems such as financial analysis, species distributions modelling, and doweled pavement performance modelling. Therefore, the objective of this paper is to investigate the performance of MARS model in capture the rainfall-runoff processes at river catchment of Malaysia. Pahang River has been selected as area of study. 30-years data set of daily rainfall and runoff at upstream tributaries of Pahang River were used to developed and validate the capability of MARS model in flood prediction. The effect of different length of record data to performance of MARS model was also examined by arranged the data into 5-years data set, 10 years data set, 20 years data set, and 30 years data set. All these data sets used 1-year data of 2003 for validation process while the others were applied for calibration. Simulation results showed that MARS model was able to learn the rainfall-runoff processes in Pahang River catchment and the model performance improved due to the longer period of data.

Journal

Annals of Valahia University of Targoviste, Geographical Seriesde Gruyter

Published: Oct 1, 2018

There are no references for this article.