Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model
Green sea turtles, known scientifically as Chelonia mydas, prefer to nest on specific sandy beaches in Sarawak, particularly within the Sarawak Turtle Islands (STI). The number of turtles landing, among other variables (number of eggs collected, eggs incubated, and eggs hatched) is an important elem...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
0002
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| Online Access: | http://journalarticle.ukm.my/24759/ http://journalarticle.ukm.my/24759/1/MAE%2011.pdf |
| _version_ | 1848816179934658560 |
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| author | Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan, |
| author_facet | Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan, |
| author_sort | Abang Mohammad Hudzaifah Abang Shakawi, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Green sea turtles, known scientifically as Chelonia mydas, prefer to nest on specific sandy beaches in Sarawak, particularly within the Sarawak Turtle Islands (STI). The number of turtles landing, among other variables (number of eggs collected, eggs incubated, and eggs hatched) is an important element in assessing the population size in Sarawak. However, modeling and predicting the number of turtles landing presents challenges due to limited data availability, resulting in less accurate forecasts for medium and long-term periods. To overcome this problem, this study presents a Grey Model (GM) approach, leveraging its capacity to effectively model systems with limited data, irregular patterns, and a lack of prior knowledge. Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. The model also suggested that the current conditions will likely increase turtle landings. This approach will find useful applications in evaluating the conservation status of the species. |
| first_indexed | 2025-11-15T01:01:46Z |
| format | Article |
| id | oai:generic.eprints.org:24759 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:01:46Z |
| publishDate | 0002 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:247592025-01-23T06:41:46Z http://journalarticle.ukm.my/24759/ Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan, Green sea turtles, known scientifically as Chelonia mydas, prefer to nest on specific sandy beaches in Sarawak, particularly within the Sarawak Turtle Islands (STI). The number of turtles landing, among other variables (number of eggs collected, eggs incubated, and eggs hatched) is an important element in assessing the population size in Sarawak. However, modeling and predicting the number of turtles landing presents challenges due to limited data availability, resulting in less accurate forecasts for medium and long-term periods. To overcome this problem, this study presents a Grey Model (GM) approach, leveraging its capacity to effectively model systems with limited data, irregular patterns, and a lack of prior knowledge. Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. The model also suggested that the current conditions will likely increase turtle landings. This approach will find useful applications in evaluating the conservation status of the species. Penerbit Universiti Kebangsaan Malaysia 0002 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24759/1/MAE%2011.pdf Abang Mohammad Hudzaifah Abang Shakawi, and Ani Shabri, and Ruhana Hassan, (0002) Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model. Malaysian Applied Biology, 53 (4). pp. 115-124. ISSN 0126-8643 https://jms.mabjournal.com/index.php/mab/issue/view/63 |
| spellingShingle | Abang Mohammad Hudzaifah Abang Shakawi, Ani Shabri, Ruhana Hassan, Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title | Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title_full | Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title_fullStr | Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title_full_unstemmed | Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title_short | Forecasting green sea turtle (Chelonia mydas) landing in Sarawak using grey model |
| title_sort | forecasting green sea turtle (chelonia mydas) landing in sarawak using grey model |
| url | http://journalarticle.ukm.my/24759/ http://journalarticle.ukm.my/24759/ http://journalarticle.ukm.my/24759/1/MAE%2011.pdf |