Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm
Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the g...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20470/ http://journalarticle.ukm.my/20470/1/24.pdf |
| _version_ | 1848815111153647616 |
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| author | Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob, |
| author_facet | Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob, |
| author_sort | Basri Badyalina, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables.
However, linear regression does not capture the complex nonlinear relationship between predictor and target variables.
It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee
colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be
an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model
by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer
functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines
the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC
algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance
of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it
provides more robust, accurate, and efficient results. |
| first_indexed | 2025-11-15T00:44:47Z |
| format | Article |
| id | oai:generic.eprints.org:20470 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:44:47Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:204702022-11-10T07:39:09Z http://journalarticle.ukm.my/20470/ Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob, Linear regression is widely used in flood quantile study that consists of meteorological and physiographical variables. However, linear regression does not capture the complex nonlinear relationship between predictor and target variables. It is rare to find a hydrological application using the group method of data handling (GMDH) model, artificial bee colony (ABC) algorithm, and ensemble technique, precisely predicting ungauged sites. GMDH model is known to be an effective model in complying with a nonlinear relationship. Therefore, in this paper, we enhance the GMDH model by implementing the ABC algorithm to optimize the parameter of partial description GMDH model with some transfer functions, namely polynomial, radial basis, sigmoid and hyperbolic tangent function. Then, ensemble averaging combines the output from those various transfer functions and becomes the new ensemble GMDH model coupled with the ABC algorithm (EGMDH-ABC) model. The results show that this method significantly improves the prediction performance of the GMDH model. The EGMDH-ABC model satisfies the nonlinearity in data to produce a better estimation. Also, it provides more robust, accurate, and efficient results. Penerbit Universiti Kebangsaan Malaysia 2022-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20470/1/24.pdf Basri Badyalina, and Nurkhairany Amyra Mokhtar, and Nur Amalina Mat Jan, and Muhammad Fadhil Marsani, and Mohamad Faizal Ramli, and Muhammad Majid, and Fatin Farazh Ya'acob, (2022) Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm. Sains Malaysiana, 51 (8). pp. 2655-2668. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid51bil8_2022/KandunganJilid51Bil8_2022.html |
| spellingShingle | Basri Badyalina, Nurkhairany Amyra Mokhtar, Nur Amalina Mat Jan, Muhammad Fadhil Marsani, Mohamad Faizal Ramli, Muhammad Majid, Fatin Farazh Ya'acob, Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title | Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title_full | Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title_fullStr | Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title_full_unstemmed | Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title_short | Hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| title_sort | hydroclimatic data prediction using a new ensemble group method of data handling coupled with artificial bee colony algorithm |
| url | http://journalarticle.ukm.my/20470/ http://journalarticle.ukm.my/20470/ http://journalarticle.ukm.my/20470/1/24.pdf |