A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico

Gokaraju, Balakrishna; Durbha, Surya S.; King, Roger L.; Younan, Nicolas H.

doi:10.1109/jstars.2010.2103927

Cited by 73 publications

(37 citation statements)

References 27 publications

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“…The proposed scheme is based on Machine Learning, The SVM is used a classifier. Through the data sets the prediction was efficient and the false alarms were very less [4].…”

Section: Literature Surveymentioning

confidence: 99%

Comprehensive Study of Applications of Spatio Temporal Data Mining in Gis

Malik¹,

Nandal²

2018

ijarcs

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Abstract:The advancement in database and internet technology has led to large debris of data being collected. The process of extraction knowledge led to coining of new term called data mining. In this paper we have discussed the spatio temporal dimension of data mining and its applications in field of Geographical Information Systems. The applications include forest fires, Maize yield, Weather forecasting etc.

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“…The proposed scheme is based on Machine Learning, The SVM is used a classifier. Through the data sets the prediction was efficient and the false alarms were very less [4].…”

Section: Literature Surveymentioning

confidence: 99%

Comprehensive Study of Applications of Spatio Temporal Data Mining in Gis

Malik¹,

Nandal²

2018

ijarcs

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“…The SVM is a non-probabilistic classifier that works by constructing a decision surface on a high-dimensional space [31] [32]. The principle of this algorithm is to find a decision surface, called hyperplane that optimally divides the training set.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Tax Complaints Classification on Twitter Using Text Mining

Dellia

Tjahyanto

2017

IJOS

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AbstractTwitter growth and utilization encourage the emergence of limitless textual information so that people can express their complaints easily This leads the Directorate General of Taxation uses twitter to deal with tax complaints faced by the community. However, the messages on twitter can contain any information, either the tax complaint or not. This will cause difficulties in handling complaints process. It is important to automatically identify so tax complaint handling can be done effectively and efficiently. Given these problems, it is necessary to do the twitter tax complaint classification with the support of text mining. There are several methods of classification such as Naïve Bayes classifiers, Support Vector Machine (SVM) and Decision Tree. This research aims to classify the tax complaint on twitter automatically by using text mining. The experimental results show the value of f-measure of SVM, Naïve Bayes and Decision Tree, respectively, are 89.3%, 85.6% and 76.9%.

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“…In such cases, artificial neural networks are used for binary classification of Levee change detection w.r.t backscatter reflectance [15], and [18]. Another approach includes the study of spatio-temporal contextual information for disaster events such as harmful algal blooms, where Support vector machines (SVMs) are used for temporal classification of harmful vs non-harmful algal blooms in coastal waters [16], and [19]. Many other recent data fusion studies for change detection include band ratioing and maximum likelihood classifiers, fuzzy logic and log-cumulants, Kullback-Leibler distance measure for expectation maximization, Markov random fields etc.…”

Section: B Data Fusionmentioning

confidence: 99%

Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management

Gokaraju

Turlapaty

Doss

et al. 2015

2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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The up-to-date results are presented from an ongoing study of the Data Fusion of multi-temporal and multi-sensor satellite datasets for near real time damage and debris assessment after a tornado disaster event. The space-borne sensor datasets comprising of: (i) C-band SAR dataset from RADARSAT-2; (ii) Multi-Spectral (MS) optical dataset including NIR from RapidEye; (iii) MS and panchromatic dataset of Advanced Linear Imaging (ALI), are studied for multi-sensor data fusion. A combined approach of multipolarized radiometric and textural feature extraction, and statistical learning based feature classification is devised for fine tuning of the complex and generalized change detection model. We also investigated the use of multi-variate conditional copula as a classifier technique, by formulating the change and no-change as a binary-class classification problem in this study. The classification results from the above technique are used for assessment of damage and debris cover after the tornado disaster event. The performance of the above approach yields a very significant Kappa accuracy up to 75%. A 10-fold cross validation strategy is used for quantitative analysis of the performance of the classification model. This study will be further extended for modelling the effect of incidence angle discrepancies or climatic condition variances, which will address the heterogeneity factor in terms of local statistics of the dataset.

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A Machine Learning Based Spatio-Temporal Data Mining Approach for Detection of Harmful Algal Blooms in the Gulf of Mexico

Cited by 73 publications

References 27 publications

Comprehensive Study of Applications of Spatio Temporal Data Mining in Gis

Comprehensive Study of Applications of Spatio Temporal Data Mining in Gis

Tax Complaints Classification on Twitter Using Text Mining

Change detection analysis of tornado disaster using conditional copulas and Data Fusion for cost-effective disaster management

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