Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling
Ahmadi, Farshad ; Mehdizadeh, Saeid and Mohammadi, Babak LU
(2021)
In Water Resources Management
35(12).
p.4127-4147
- Abstract
The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were... (More)
The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.
(Less)
- author
- Ahmadi, Farshad
; Mehdizadeh, Saeid
and Mohammadi, Babak
LU
- organization
- publishing date
- 2021-08-17
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Firefly algorithm, Hybrid models, Reconnaissance drought index, Support vector regression, Wavelet, Whale optimization algorithm
- in
- Water Resources Management
- volume
- 35
- issue
- 12
- pages
- 4127 - 4147
- publisher
- Springer
- external identifiers
-
- scopus:85112780185
- ISSN
- 0920-4741
- DOI
- 10.1007/s11269-021-02934-z
- language
- English
- LU publication?
- yes
- id
- 37acfb77-b6b9-4a76-acde-749a41532fe8
- date added to LUP
- 2021-08-23 10:10:40
- date last changed
- 2022-04-27 03:20:32
@article{37acfb77-b6b9-4a76-acde-749a41532fe8,
abstract = {{<p>The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.</p>}},
author = {{Ahmadi, Farshad and Mehdizadeh, Saeid and Mohammadi, Babak}},
issn = {{0920-4741}},
keywords = {{Firefly algorithm; Hybrid models; Reconnaissance drought index; Support vector regression; Wavelet; Whale optimization algorithm}},
language = {{eng}},
month = {{08}},
number = {{12}},
pages = {{4127--4147}},
publisher = {{Springer}},
series = {{Water Resources Management}},
title = {{Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling}},
url = {{http://dx.doi.org/10.1007/s11269-021-02934-z}},
doi = {{10.1007/s11269-021-02934-z}},
volume = {{35}},
year = {{2021}},
}