Comparison of Tropical Thunderstorm Estimation between Multiple Linear Regression, Dvorak, and ANFIS
TLDR
In this article, a combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H), Cloud ( C ), Precipitable Water Vapor (PWV ), and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system.Abstract:
Thunderstorms are dangerous and it has increased due to highly precipitation and cloud cover density in the Mesoscale Convective System area. Climate change is one of the causes to increasing the thunderstorm activity. The present studies aimed to estimate the thunderstorm activity at the Tawau area of Sabah, Malaysia based on the Multiple Linear Regression (MLR), Dvorak technique, and Adaptive Neuro-Fuzzy Inference System (ANFIS). A combination of up to six inputs of meteorological data such as Pressure (P), Temperature (T), Relative Humidity (H) , Cloud ( C ), Precipitable Water Vapor (PWV ) , and Precipitation (Pr) on a daily basis in 2012 were examined in the training process to find the best configuration system. By using Jacobi algorithm, H and PWV were identified to be correlated well with thunderstorms. Based on the two inputs that have been identified, the Sugeno method was applied to develop a Fuzzy Inference System. The model demonstrated that the thunderstorm activities during intermonsoon are detected higher than the other seasons. This model is comparable to the thunderstorm data that was collected manually with percent error below 50% .read more
Citations
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Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status
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Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy
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A regression approach for prediction of Youtube views
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Satellite image inpainting with deep generative adversarial neural networks
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References
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Journal ArticleDOI
Fuzzy identification of systems and its applications to modeling and control
T. Takagi,Michio Sugeno +1 more
TL;DR: A mathematical tool to build a fuzzy model of a system where fuzzy implications and reasoning are used is presented and two applications of the method to industrial processes are discussed: a water cleaning process and a converter in a steel-making process.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Book
Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
Journal ArticleDOI
Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]
TL;DR: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming.
Journal ArticleDOI
Nowcasting Thunderstorms: A Status Report
TL;DR: In this paper, the authors reviewed the status of forecasting convective precipitation for time periods less than a few hours (nowcasting), and developed techniques for nowcasting thunderstorm location were developed in the 1960s and 1970s by extrapolating radar echoes.
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