Artificial neural network model for predicting windstorm intensity and the potential damages

Thesis (PhD. (Civil Engineering))

Bibliographic Details
Main Author: Bachok, Mohd. Fairuz
Format: Thesis
Language:English
Published: Universiti Teknologi Malaysia 2023
Subjects:
Online Access:http://openscience.utm.my/handle/123456789/666
_version_ 1796848895365480448
author Bachok, Mohd. Fairuz
author_facet Bachok, Mohd. Fairuz
author_sort Bachok, Mohd. Fairuz
collection OpenScience
description Thesis (PhD. (Civil Engineering))
first_indexed 2024-03-05T17:34:29Z
format Thesis
id oai:openscience.utm.my:123456789/666
institution Universiti Teknologi Malaysia - OpenScience
language English
last_indexed 2024-03-05T17:34:29Z
publishDate 2023
publisher Universiti Teknologi Malaysia
record_format dspace
spelling oai:openscience.utm.my:123456789/6662023-08-29T15:00:43Z Artificial neural network model for predicting windstorm intensity and the potential damages Bachok, Mohd. Fairuz Windstorms—Research Hazard signs Natural disaster warning systems Thesis (PhD. (Civil Engineering)) A predictive model was developed to provide a mechanism that can be regionally simulated, and predict low or high risk windstorm, its possible location, time, duration of the risk, intensity as well as potential damages. This development corresponds to the windstorm hazard monitoring mechanism which is not available in the country. The predictive model includes 16 prediction processes with 20 back-propagation algorithms whereby radar imageries and meteorological station data were used as a raw data input. Development of the predictive model is according to the most frequent cause of windstorm in the country, applicable observational data, existing severe weather monitoring system and a current widely used machinelearning model in forecasting. For the establishment of back-propagation algorithms, it does not only use Artificial Neural Network (ANN) model but also other techniques such as multiple regression analysis, dichotomous forecast method and error difference method. Besides, multiple correlation coefficient (R) between input units and output unit is set to be higher than 0.7, taking into consideration of atmospheric complexity and to avoid back-propagation algorithm from the poorly generated output that could be beyond the acceptable range since each prediction process relies on each other. Local conditions wind multiplier map, hazard threshold, and damage scales are three supporting tools that were developed to compliment the predictive model. The predictive model is highly accurate because the R-values for 14 prediction processes are higher than 0.7 ranging from 0.704 to 1.00. In addition, the mean square error (MSE) values for ANN model algorithms (pattern recognition tool) for 5 prediction processes are low from 0.00 to 0.0286 and errors from 0.00 to 0.0309. The other 11 prediction processes which utilised ANN model algorithms (fitting tool), gave R-values for all the algorithms higher than 0.900 except one algorithm equal to 0.8661, meanwhile MSE between 0.22 to 116.41. For verification, high Critical Success Index (CSI) and error difference whether in unit (minutes, km and m/s) or percentage were in acceptable range reinforced that the predictive model is able to simulate output at high precision. The CSI for 5 prediction processes are equal to 1.00 and error difference for 9 prediction processes are within ± 0.0 to ± 24.0 %. According to the error difference in unit, in terms of time for 6 prediction processes, the difference is between ± 0 to ± 5 minutes, distance for 2 prediction processes, the difference is between ± 1 to ± 4 km and the intensity for 1 prediction process is ± 1 m/s. Lead time is between 10 to 60 minutes. However, prediction process 5 and 16 are the only prediction processes need an attention for enhancement since the CSI average is low, 0.45 to 0.65 and 0.36 to 0.51 respectively. This is due to the missing and false number of grid area resulted from prediction, even though high hit number of grid area and the centroid distances between predicted convective cell area and actual area is short in range from 2.1 to 4.0 km. Faculty of Engineering - School of Civil Engineering 2023-08-29T06:58:00Z 2023-08-29T06:58:00Z 2019 Thesis Dataset NA NA http://openscience.utm.my/handle/123456789/666 en NA; NA application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf Universiti Teknologi Malaysia
spellingShingle Windstorms—Research
Hazard signs
Natural disaster warning systems
Bachok, Mohd. Fairuz
Artificial neural network model for predicting windstorm intensity and the potential damages
title Artificial neural network model for predicting windstorm intensity and the potential damages
title_full Artificial neural network model for predicting windstorm intensity and the potential damages
title_fullStr Artificial neural network model for predicting windstorm intensity and the potential damages
title_full_unstemmed Artificial neural network model for predicting windstorm intensity and the potential damages
title_short Artificial neural network model for predicting windstorm intensity and the potential damages
title_sort artificial neural network model for predicting windstorm intensity and the potential damages
topic Windstorms—Research
Hazard signs
Natural disaster warning systems
url http://openscience.utm.my/handle/123456789/666
work_keys_str_mv AT bachokmohdfairuz artificialneuralnetworkmodelforpredictingwindstormintensityandthepotentialdamages