Full text

Turn on search term navigation

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Accurate and reliable predictors selection and model construction are the key to medium and long-term runoff forecast. In this study, 130 climate indexes are utilized as the primary forecast factors. Partial Mutual Information (PMI), Recursive Feature Elimination (RFE) and Classification and Regression Tree (CART) are respectively employed as the typical algorithms of Filter, Wrapper and Embedded based on Feature Selection (FS) to obtain three final forecast schemes. Random Forest (RF) and Extreme Gradient Boosting (XGB) are respectively constructed as the representative models of Bagging and Boosting based on Ensemble Learning (EL) to realize the forecast of the three types of forecast lead time which contains monthly, seasonal and annual runoff sequences of the Three Gorges Reservoir in the Yangtze River Basin. This study aims to summarize and compare the applicability and accuracy of different FS methods and EL models in medium and long-term runoff forecast. The results show the following: (1) RFE method shows the best forecast performance in all different models and different forecast lead time. (2) RF and XGB models are suitable for medium and long-term runoff forecast but XGB presents the better forecast skills both in calibration and validation. (3) With the increase of the runoff magnitudes, the accuracy and reliability of forecast are improved. However, it is still difficult to establish accurate and reliable forecasts only large-scale climate indexes used. We conclude that the theoretical framework based on Machine Learning could be useful to water managers who focus on medium and long-term runoff forecast.

Details

Title
A Medium and Long-Term Runoff Forecast Method Based on Massive Meteorological Data and Machine Learning Algorithms
Author
Li, Yujie 1 ; Wang, Dong 2 ; Wei, Jing 3 ; Li, Bo 3 ; Xu, Bin 4 ; Xu, Yueping 5 ; Huang, Huaping 6 

 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; [email protected]; Zhejiang Design Institute of Water Conservancy and Hydroelectric Power, Hangzhou 310002, China; [email protected] (J.W.); [email protected] (B.L.) 
 Changjiang Water Resources Commission, Wuhan 430010, China; [email protected] 
 Zhejiang Design Institute of Water Conservancy and Hydroelectric Power, Hangzhou 310002, China; [email protected] (J.W.); [email protected] (B.L.) 
 Hangzhou Design Institute of Water Conservancy and Hydropower, Hangzhou 310016, China; [email protected] 
 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; [email protected] 
 China Water Resources Pearl River Planning Surveying & Designing Co., Ltd, Guangzhou 510610, China; [email protected] 
First page
1308
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20734441
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2530129754
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.