Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network
Theprimary production quantity depends on the vertical distribution of chlorophyll concentration in the water column. The chlorophyll maximum value not always observed near or at the sea surface, but sometimes lies deeper than bottom of the euphotic zone. In this case, the ocean color sensors cannot...
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| Format: | Article |
| Language: | English |
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Lembaga Penelitian dan Pengabdian kepada Masyarakat
2009-10-01
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| Series: | Rekayasa |
| Online Access: | https://journal.trunojoyo.ac.id/rekayasa/article/view/2203 |
| _version_ | 1818025166722039808 |
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| author | Achmad Fachruddin Syah |
| author_facet | Achmad Fachruddin Syah |
| author_sort | Achmad Fachruddin Syah |
| collection | DOAJ |
| description | Theprimary production quantity depends on the vertical distribution of chlorophyll concentration in the water column. The chlorophyll maximum value not always observed near or at the sea surface, but sometimes lies deeper than bottom of the euphotic zone. In this case, the ocean color sensors cannot measure the chlorophyll maximum value. A shifted Gauss model has been proposed
to describe the variation of the chlorophyll-a (Chl-a) profile which consists offour parameters, i.e.
background biomass (Br)1 maximum depth of Chl-a (Zm), total biomass zn the peak (h), and measurenment of the thickness or vertical scale of the peak (o). However, these parameters are not easy to be determined directlyfrom satellite data. therefore, in these research, anANN methodology is used. Using in-situ data 1962 to 1985 in Banda Sea, the above parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longi_tude, and season. The total of 53 profiles of Chl-a and temperature are used for ANN. The correlation coefficient of these parameters are 0.852 (B,J, 0.670 (h), 0.983 (d) and 0.990 (Zm) respectively. After comparing with in-situ data and AN!'J model, the result show not good enough agreement relatively.
Keywords: Chlorophyll-a (Chl-a), Vertical Structure, Artificial Neural Networks (ANN) |
| first_indexed | 2024-12-10T04:11:48Z |
| format | Article |
| id | doaj.art-e0029a28e1324f2996611ee028125b70 |
| institution | Directory Open Access Journal |
| issn | 0216-9495 2502-5325 |
| language | English |
| last_indexed | 2024-12-10T04:11:48Z |
| publishDate | 2009-10-01 |
| publisher | Lembaga Penelitian dan Pengabdian kepada Masyarakat |
| record_format | Article |
| series | Rekayasa |
| spelling | doaj.art-e0029a28e1324f2996611ee028125b702022-12-22T02:02:43ZengLembaga Penelitian dan Pengabdian kepada MasyarakatRekayasa0216-94952502-53252009-10-012215316310.21107/rekayasa.v2i2.22031804Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural NetworkAchmad Fachruddin Syah0Universitas Trunojoyo MaduraTheprimary production quantity depends on the vertical distribution of chlorophyll concentration in the water column. The chlorophyll maximum value not always observed near or at the sea surface, but sometimes lies deeper than bottom of the euphotic zone. In this case, the ocean color sensors cannot measure the chlorophyll maximum value. A shifted Gauss model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists offour parameters, i.e. background biomass (Br)1 maximum depth of Chl-a (Zm), total biomass zn the peak (h), and measurenment of the thickness or vertical scale of the peak (o). However, these parameters are not easy to be determined directlyfrom satellite data. therefore, in these research, anANN methodology is used. Using in-situ data 1962 to 1985 in Banda Sea, the above parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longi_tude, and season. The total of 53 profiles of Chl-a and temperature are used for ANN. The correlation coefficient of these parameters are 0.852 (B,J, 0.670 (h), 0.983 (d) and 0.990 (Zm) respectively. After comparing with in-situ data and AN!'J model, the result show not good enough agreement relatively. Keywords: Chlorophyll-a (Chl-a), Vertical Structure, Artificial Neural Networks (ANN)https://journal.trunojoyo.ac.id/rekayasa/article/view/2203 |
| spellingShingle | Achmad Fachruddin Syah Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network Rekayasa |
| title | Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network |
| title_full | Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network |
| title_fullStr | Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network |
| title_full_unstemmed | Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network |
| title_short | Pendugaan Konsentrasi Klorofil-A Secara Vertikal dengan Neural Network |
| title_sort | pendugaan konsentrasi klorofil a secara vertikal dengan neural network |
| url | https://journal.trunojoyo.ac.id/rekayasa/article/view/2203 |
| work_keys_str_mv | AT achmadfachruddinsyah pendugaankonsentrasiklorofilasecaravertikaldenganneuralnetwork |