کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8845854 | 1617191 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms
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کلمات کلیدی
Karenia selliformisCPTSGLMMEVAPBICGLMAICINMSAL - WILLinsolation - انزالEvaporation - تبخیرanalysis of variance - تحلیل واریانسANOVA - تحلیل واریانس Analysis of varianceMaximum a posteriori - حداکثر a posterioriMaximum likelihood - حداکثر احتمالWater temperature - دمای آبHumid - رطوبتHumidity - رطوبتDAG - روزBayesian network - شبکه بیزی، شبکه بیزینSalinity - شوریNaïve Bayes classifier - طبقه بندی Bayes نائومیGeneral linear model - مدل خطی کلیAkaike information criterion - معیار اطلاعاتی آکائیکBayesian information criteria - معیارهای اطلاعات بیزیmap - نقشهWatt - واتDirected acyclic graph - گراف خطی خطی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
بوم شناسی، تکامل، رفتار و سامانه شناسی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The prediction of the dinoflagellate red tide forming Karenia selliformis is a relevant task to aid optimized management decisions in marine coastal water. The objective of the present study is to compare different modeling approaches for prediction of Karenia selliformis occurrences and blooms. A set of physical parameters (salinity, temperature and tide amplitude), meteorological constraints (evaporation, air temperature, insolation, rainfall, atmospheric pressure and humidity), sampling months and sampling sites are used. The model prediction included general linear model (GLM), Bayesian Network (BN) and the simplest BN type which is, Naive Bayes classifier (NB). The results showed that three models incriminated high salinity in Karenia selliformis blooms and the sampling sites, mainly Boughrara lagoon, in the occurrences. The BN performed better than linear models (NB and GLM) for both Karenia selliformis occurrences and blooms prediction. This later is related to the facts that BN considered the inter-independency between predictive variables and that the relationships between the variables and the outcome are often non-linear such us; the transition to bloom situations appeared to be triggered by a salinity threshold. This study is useful in the management of this ecosystem so as to use the best disposal options in the early prediction of the toxic blooms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Informatics - Volume 43, January 2018, Pages 12-23
Journal: Ecological Informatics - Volume 43, January 2018, Pages 12-23
نویسندگان
Wafa Feki-Sahnoun, Hasna Njah, Asma Hamza, Nouha Barraj, Mabrouka Mahfoudi, Ahmed Rebai, Malika Bel Hassen,