Rainfall prediction using optimally pruned extreme learning machines

Huajin Li (), Yusen He (), He Yang (), Yong Wei (), Songlin Li () and Jianqiang Xu ()
Additional contact information
Huajin Li: Chengdu University
Yusen He: The University of Iowa
He Yang: Chengdu University
Yong Wei: Chengdu University of Technology
Songlin Li: Department of Natural Resources of Guizhou Province
Jianqiang Xu: Chengdu University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 34, 799-817

Abstract: Abstract Rainfall impacts local water quantity and quality. Accurate and timely prediction of rainfall is highly desirable in water management and hydrogeologic hazards mitigation, which is critical for the environmentally sustainable development. Previous rainfall prediction processes are complex and computationally costly for its intrinsic high uncertainty and variability. In this paper, a data-driven approach is applied to predict the rainfall based on historic data via time-series modeling and optimally pruned extreme learning machine (OP-ELM). The rainfall datasets collected from six counties within Three Georges Reservoir are utilized as case studies in this research, first, the rainfall data is pre-processed with outlier removal and missing value imputation, and the monthly ahead difference is computed based on the instant average of monthly rainfall. Next, the autocorrelation function and partial autocorrelation function were computed and the Ljung–Box test statistic is utilized to explore the significance of the historic lagged-series. All the statistically significant historic lags are selected as inputs for prediction algorithms. Last, an OP-ELM algorithm is developed to predict the monthly rainfall with tenfold cross validation. Four activation functions: the sigmoid, sine, hardlim, and radial basis function, are considered in the OP-ELM. The prediction performance is evaluated with metrics including mean absolute error, mean absolute percentage error, root mean square error, and max error rate. Overall, the computational results indicate the proposed framework outperforms the other benchmarking machine learning algorithms through six case studies in Three Gorges Reservoir, China.

Keywords: Rainfall prediction; Sustainable development; Time-series modeling; OP-ELM (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-04706-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04706-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-04706-9

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().