Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm
Mohammadi, Babak LU
(2023)
In Hydrology
10(3).
- Abstract
Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI... (More)
Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).
(Less)
- author
- Mohammadi, Babak
LU
- organization
- publishing date
- 2023-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial neural network, drought prediction, Optimazition algorithm, hydroinformatics, hydrological modeling, standard precipitation index (SPI)
- in
- Hydrology
- volume
- 10
- issue
- 3
- article number
- 58
- publisher
- MDPI AG
- external identifiers
-
- scopus:85151124461
- ISSN
- 2306-5338
- DOI
- 10.3390/hydrology10030058
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2023 by the author.
- id
- d551e916-5a6a-4d6e-ac61-9a689369d2ab
- date added to LUP
- 2023-04-11 17:05:12
- date last changed
- 2024-01-24 10:59:22
@article{d551e916-5a6a-4d6e-ac61-9a689369d2ab,
abstract = {{<p>Drought monitoring and prediction have important roles in various aspects of hydrological studies. In the current research, the standardized precipitation index (SPI) was monitored and predicted in Peru between 1990 and 2015. The current study proposed a hybrid model, called ANN-FA, for SPI prediction in various time scales (SPI3, SPI6, SPI18, and SPI24). A state-of-the-art firefly algorithm (FA) has been documented as a powerful tool to support hydrological modeling issues. The ANN-FA uses an artificial neural network (ANN) which is coupled with FA for Lima SPI prediction via other stations. Through the intelligent utilization of SPI series from neighbors’ stations as model inputs, the suggested approach might be used to forecast SPI at various time scales in a meteorological station with insufficient data. To conduct this, the SPI3, SPI6, SPI18, and SPI24 were modeled in Lima meteorological station using other meteorological stations’ datasets in Peru. Various error criteria were employed to investigate the performance of the ANN-FA model. Results showed that the ANN-FA is an effective and promising approach for drought prediction and also a multi-station strategy is an effective strategy for SPI prediction in the meteorological station with a lack of data. The results of the current study showed that the ANN-FA approach can help to predict drought with the mean absolute error = 0.22, root mean square error = 0.29, the Pearson correlation coefficient = 0.94, and index of agreement = 0.97 at the testing phase of best estimation (SPI3).</p>}},
author = {{Mohammadi, Babak}},
issn = {{2306-5338}},
keywords = {{artificial neural network; drought prediction; Optimazition algorithm; hydroinformatics; hydrological modeling; standard precipitation index (SPI)}},
language = {{eng}},
number = {{3}},
publisher = {{MDPI AG}},
series = {{Hydrology}},
title = {{Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm}},
url = {{http://dx.doi.org/10.3390/hydrology10030058}},
doi = {{10.3390/hydrology10030058}},
volume = {{10}},
year = {{2023}},
}