scholarly journals Modeling of Sea Surface Temperature through Fitting Linear Model with Interaction

2021 ◽  
Vol 18 (1) ◽  
pp. 55-66
Author(s):  
Miftahuddin Miftahuddin ◽  
Wanda Sri Noviana
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Moving Average ◽  
Statistical Significance ◽  
Peak Effect ◽  
Sea Surface ◽  
P Value ◽  
Linear Fitting ◽  
Statistical Measures ◽  
Fitting Model

Sea surface temperature (SST) is one of the attributes of the world climate system and global warming. The relationship between SST and other climate parameters can be represented in a linearity approach. Through this approach, SST variability shows monthly and yearly effects. Information on these two time effects is important for knowing the period of peak effect as well as other statistical measures in the linear fitting model. The models used include transformation and without covariate transformation, interaction and without covariate interaction, and with centering and with the addition of time covariates in the model. The linear fitting model chosen as the basis for construction is a model with a combination effect of covariate interaction and transformation giving an increase in the magnitude of multiple R2 (56.62%) and adjusted R2 (56.13%) respectively 0.31% and 0.43%. This indicates that the time covariate has a very strong significant effect on the model compared to the continuous covariate. In general, the model has a statistical significance of p-value < 2.2e-16, as well as for the time covariate. However, because the model has an autocorrelation and a large AIC value, this effect is removed by means of an autoregressive moving average. The obtained linear fitting model for SST data is the model with AIC 403.2987.

scholarly journals Perbandingan Model GAM dan Gamboost dalam Fitting Dataset Sea Surface Temperature

2018 ◽  
Vol 18 (1) ◽  
pp. 39-50
Author(s):  
Miftahuddin Miftahuddin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Risk Model ◽  
Generalized Additive Models ◽  
Information Criteria ◽  
Additive Models ◽  
Sea Surface ◽  
Akaike Information Criteria ◽  
Fitting Model

Fitting model GAM (generalized additive models) dan Gamboost (generalized additive models by boosting) untuk dataset SST (sea surface temperature) dimaksudkan sebagai upaya mencapai perbaikan fitting model terhadap data SST. Secara umum, model GAM dapat memvisualisasikan masing-masing kovariat, sedangkan model gamboost dapat memvisualisasikan lebih detail kovariatnya dalam beberapa bentuk, baik secara linier dan nonlinier. Pengukuran performance yang digunakan terhadap model adalah nilai AIC (Akaike Information Criteria) dan CV-risk. Model GAM dengan boosting menunjukkan lebih sesuai dalam struktur model, pemilihan model terbaik dan seleksi variabel pada dataset SST. Fitting model GAM dapat menghasilkan pola dan trend masing-masing kovariat meskipun memiliki beberapa gap, sedangkan pada model gamboost memiliki lebih banyak pilihan simultan dalam bentuk linier, nonlinier dan smooth untuk masing-masing kovariat. Kedua pendekatan fitting memiliki kelebihan yang dapat saling melengkapi dalam memodelkan dataset SST.

scholarly journals Daerah Penangkapan Potensial Tuna Madidihang Thunnus albacares, Bonnaterre, 1788 (Teleostei:Scombridae) di Laut Seram

2020 ◽  
Vol 23 (2) ◽  
pp. 207-216
Author(s):  
Jacobus Bunga Paillin ◽  
Delly Dominggas Paulina Matrutty ◽  
Stany Rachel Siahainenia ◽  
Ruslan Husen Saban Tawari ◽  
Haruna Haruna ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Yellowfin Tuna ◽  
Sea Surface ◽  
P Value ◽  
Thunnus Albacares ◽  
Test Analysis ◽  
Medium Potential

This research aims are to determine the potential fishing grounds of yellowfin tuna based on the approach of sea surface temperature, chlorophyll-a and catches in the Ceram Sea. Overall catches of 407 Individuals. In January the total catches were 66 individuals (14.44%), in February 67 individuals (14.66%), in March 84 individuals (18.38%), in April 116 individuals (25.38%) and in May 124 individuals (27.13%). The distribution of sea surface temperature and chlorophyll-a in the Ceram Sea in January-May 2019 looks varied. In January the average sea surface temperature was 29.13 oC, in February sea surface temperature was 29.54 oC, in March sea surface temperature was 30.12 oC, in April sea surface temperature was 30.12 oC, in May sea surface temperature was 29.77 oC. Chlorophyll-a concentration in January and February was 0.21 mg/m3, March was 0.20 mg/m3, April was 0.16 mg/m3, and May was 0.25 mg/m3. The results of the t-test analysis showed the P-value of sea surface temperature was 0.009<0.05, chlorophyll-a P-value 0.00048<0.05. Determination of potential fishing areas based on sea surface temperature, chlorophyll-a, and CPUE indicators shows that potential fishing areas are found in January, February, March, and May, while in April are in the medium potential category. Penelitian ini dilaksanakan dengan tujuan menentukan daerah penangkapan potensial Tuna madidihang berdasarkan pendekatan suhu permukaan laut, klorofil-a dan hasil tangkapan di Laut Seram.  Secara keseluruhan hasil tangkapan ikan tuna madidihang sebanyak 407 Individu. Bulan Januari total hasil tangkapan sebanyak 66 individu (14.44%), bulan Februari 67 individu (14.66%), bulan Maret 84 individu (18.38%), bulan April 116 individu (25.38%) dan bulan Mei 124 individu (27.13%). Sebaran suhu permukaan laut dan klorofil-a di Laut Seram pada bulan Januari-Mei 2019 terlihat bervariasi. Bulan Januari rata-rata suhu permukaan laut sebesar 29.13 oC, bulan Februari suhu permukaan laut 29.54 o, bulan Maret suhu permukaan laut 30.12 oC, bulan April suhu permukaan laut 30.12 oC, bulan Mei suhu permukaan laut 29.77 oC. Konsentrasi klorofil-a pada bulan Januari dan Februari sebesar 0.21 mg/m3, bulan Maret sebesar 0.20 mg/m3, bulan April sebesar 0.16 mg/m3, dan bulan Mei sebesar 0.25 mg/m3.  Hasil analisis uji t menunjukan nilai P-value suhu permukaan laut sebesar 0,009<0,05, klorofil-a P-value 0,00048<0,05. Penentuan daerah penangkapan ikan potensial berdasarkan indikator suhu permukaan laut, klorofil-a dan CPUE menunjukkan daerah penangkapan ikan potensial terdapat pada bulan Januari, Februari, Maret, dan Mei, sedangkan pada bulan April berada dalam kategori potensial sedang. 

scholarly journals Relationship between environmental parameters and the plankton community of the Batuhideung Fishing Grounds,

2018 ◽  
Vol 20 (1) ◽  
pp. 171-180 ◽  
Author(s):  
NOVERITA D. TAKARINA ◽  
WAWAN NURLIANSYAH ◽  
WISNU WARDHANA
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Current Velocity ◽  
Diversity Index ◽  
Statistical Significance ◽  
Mesh Size ◽  
Environmental Parameters ◽  
Sea Surface ◽  
Zooplankton Abundance ◽  
Physical And Chemical

Takarina ND, Nurliansyah W, Wardhana W. 2019. Relationship between environmental parameters and the planktoncommunity of the Batuhideung Fishing Grounds, Pandeglang, Banten, Indonesia. Biodiversitas 20: 171-180. Phytoplankton has a roleas primary producers and zooplankton as primary consumers in the marine environments. The composition of the plankton communityis dependent on the physical and chemical characteristics of the waters. The aim of the research described here was to analyze thecommunity structure of plankton in the Batuhideung Fishing Grounds, of Banten in Indonesia, and to relate this structure to the physicalparameters (sea surface temperature, clarity, current velocity) and chemical parameters (salinity, pH, DO, nitrate, phosphate) of its seasurroundings. The research was conducted three times at five observation stations from September to October 2017. Samples ofphytoplankton and zooplankton were taken horizontally using nets with mesh size of 80 μm and 133 μm, respectively. Results showedthat the sea surface temperature ranged from 28.70-30.20C, current velocity 0.10-1.30 m/s, clarity 7-10 m, salinity 30-34 g/L, DO 6.20-8.60 mg/L, pH 8.40-8.59, nitrate concentration 0.49-0.81 mg/L, and phosphate concentration 0.09-0.42 mg/L. There were 37 generafrom 4 classes of phytoplankton. The abundance of Bacillariophyta was 52,734 individuals/L, Myzozoa was 1,315 ind/L, Chyanophytawas 633 ind/L, and Euglenophyta was 200 ind/L. There were 35 genera from 12 classes of zooplankton, dominated by Copepods withabundance 82.1-91.4%. The diversity index of phytoplankton ranged from 1.25-2.02, evenness index ranged from 0.52-0.85, anddominance index ranged from 0.19-0.38. Based on multivariate cluster analysis, there were similar environmental parameters in stations1, 2 and 3, while stations 4 and 5 grouped differently from the other three. Based on Pearson's correlation analysis, current velocity andphosphate were positively correlated to phytoplankton abundance, whereas temperature, transparency, salinity, DO, pH, and nitrate werenegatively correlated, temperature significantly so. Nitrate, phosphate, salinity, DO were positively correlated with zooplanktonabundance but not significantly. Temperature, current velocity, clarity, and pH were negatively correlated with zooplankton abundance,but only with pH did the correlation reach statistical significance.

Testing for Deterministic Trends in Global Sea Surface Temperature

2011 ◽  
Vol 24 (10) ◽  
pp. 2516-2522 ◽  
Author(s):  
Susana M. Barbosa
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Linear Model ◽  
Linear Trend ◽  
Statistical Significance ◽  
Sea Surface ◽  
Deterministic Trend ◽  
Parametric Statistical Test ◽  
Global Sea Surface Temperature

Abstract Long-term variability in global sea surface temperature (SST) is often quantified by the slope from a linear regression fit. Attention is then focused on assessing the statistical significance of the derived slope parameter, but the adequacy of the linear model itself, and the inherent assumption of a deterministic linear trend, is seldom tested. Here, a parametric statistical test is applied to test the hypothesis of a linear deterministic trend in global sea surface temperature. The results show that a linear slope is not adequate for describing the long-term variability of sea surface temperature over most of the earth’s surface. This does not mean that sea surface temperature is not increasing, rather that the increase should not be characterized by the slope from a linear fit. Therefore, describing the long-term variability of sea surface temperature by implicitly assuming a deterministic linear trend can give misleading results, particularly in terms of uncertainty, since the actual increase could be considerably larger than the one predicted by a deterministic linear model.

scholarly journals Improved of Forecasting Sea Surface Temperature based on Hybrid ARIMA and Support Vector Machines Models

2021 ◽  
Vol 17 (5) ◽  
pp. 609-620
Author(s):  
Wan Imanul Aisyah Wan Mohamad Nawi ◽  
Muhamad Safiih Lola ◽  
Razak Zakariya ◽  
Nurul Hila Zainuddin ◽  
Abd. Aziz K. Abd Hamid ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Moving Average ◽  
Arima Model ◽  
Nonlinear Behavior ◽  
Time Series Forecasting ◽  
Sea Surface ◽  
Support Vector ◽  
Forecast Precision

Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed models produce lesser MAE, MAPE, MSE, and RMSE values in comparison to the single ARIMA and SVMs models. In additional, ARIMA-SVM are much better than compared to the existing models since the forecasting values are closer to the actual value. Therefore, we conclude that the presented models can be used to generate superior predicting values in time series forecasting with a way higher forecast precision.

Holocene sea surface temperature variations recorded in corals from Kikai Island, Japan

2017 ◽  
Vol 51 (4) ◽  
pp. e9-e14 ◽  
Author(s):  
Hiroto Kajita ◽  
Atsuko Yamazaki ◽  
Takaaki Watanabe ◽  
Chung-Che Wu ◽  
Chuan-Chou Shen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Variations

PENENTUAN DAERAH POTENSIAL BUDIDAYA RUMPUT LAUT KAPPAPHYCUS ALVAREZII DI PROVINSI NUSA TENGGARA TIMUR

2019 ◽  
Vol 3 ◽  
pp. 929
Author(s):  
Marianus Filipe Logo ◽  
N M. R. R. Cahya Perbani ◽  
Bayu Priyono
Keyword(s):  
Dissolved Oxygen ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Kappaphycus Alvarezii ◽  
Sea Surface

Provinsi Nusa Tenggara Timur (NTT) merupakan penghasil rumput laut kappaphycus alvarezii kedua terbesar di Indonesia berdasarkan data Badan Pusat Statistik (2016). Oleh karena itu diperlukan zonasi daerah potensial budidaya rumput laut kappaphycus alvarezii untuk pengembangan lebih lanjut. Penelitian ini bertujuan untuk menentukan daerah yang potensial untuk budidaya rumput laut kappaphycus alvarezii di Provinsi NTT berdasarkan parameter sea surface temperature (SST), salinitas, kedalaman, arus, dissolved oxygen (DO), nitrat, fosfat, klorofil-a, dan muara sungai. Penentuan kesesuaian lokasi budidaya dilakukan dengan memberikan bobot dan skor bagi setiap parameter untuk budidaya rumput laut kappaphycus alvarezii menggunakan sistem informasi geografis melalui overlay peta tematik setiap parameter. Dari penelitian ini diperoleh bahwa kadar nitrat, arus, kedalaman, dan lokasi muara sungai menjadi parameter penentu utama. Jarak maksimum dari bibir pantai adalah sekitar 10 km. Potensial budidaya rumput laut kappaphycus alvarezii ditemukan di Pulau Flores bagian barat, kepulauan di Kabupaten Flores Timur dan Alor, selatan Pulau Sumba, Pulau Rote, dan Teluk Kupang.

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