A Meteorological Fuzzy Expert System Incorporating Subjective User Input

Hadjimichael, Michael; Kuciauskas, Arunas P.; Tag, Paul M.; Bankert, Richard L.; Peak, James E.

doi:10.1007/s101150200011

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…Weather prediction has been used for traffic controlling applications [20]. Fuzzy expert systems have been employed to forecast winds and predict weather [25].…”

Section: Related Workmentioning

confidence: 99%

Predicting weather events using fuzzy rule based system

Awan

Awais

2011

Applied Soft Computing

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“…Weather prediction has been used for traffic controlling applications [20]. Fuzzy expert systems have been employed to forecast winds and predict weather [25].…”

Section: Related Workmentioning

confidence: 99%

Predicting weather events using fuzzy rule based system

Awan

Awais

2011

Applied Soft Computing

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“…It would be possible to reduce the damages and prevent from many more serious damages with correct analysis of seismic events. Various methods are used to forecast the occurrence of earthquake events such as mathematical modeling, ionosphere analysis and studying animals' behavior (Abraham, 2005;Hadjimichael and et al, 2002) [3][4] [5]. Some these methods only utilize from a single feature and are not able to use different features in earthquake forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Ghorbani¹,

Barari²,

Hosseini³

2017

Preprint

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Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world.Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighborhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting preprocessing and separating training and testing samples, 4) generating relevant models with training samples and detecting and categorizing test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

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“…Section (3) presents the suggested model with a description proposed architecture. In sections (4) and (5) the results have been represented and section (6) has been allocated to conclusion and future recommendations.…”

Section: Introductionmentioning

confidence: 99%

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Ghorbani

Barari

Hosseini

2017

cej

View full text Add to dashboard Cite

Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world. Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ،C4.5 decision tree and KNN near neighbourhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting pre-processing and separating training and testing samples, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

show abstract

A Meteorological Fuzzy Expert System Incorporating Subjective User Input

Cited by 11 publications

References 7 publications

Predicting weather events using fuzzy rule based system

Predicting weather events using fuzzy rule based system

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

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