Correlation analysis between lightning flashes and rainfall rate during a flash flood thunderstorm

Yusop, Norbayah; Ahmad, Mohd Riduan; Ching, Tan Shea; Baharin, Shamsul Ammar Shamsul; Esa, Mazlina; Sidik, Muhammad Abu Bakar

doi:10.11591/ijeecs.v28.i3.pp1322-1329

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This region also receives high annual rainfall (2000-4000 mm), with the monsoons being the controlling feature of climatic variability. The warm and humid climate (25℃ until 32℃), combined with the mountainous and lowland terrain, is well-suited for the formation of thunderclouds, which leads to an increase in thunderstorms and lightning development [19]. Malaysia is in the top three among Southeast Asian countries for recorded high lightning activity, after Indonesia and Vietnam.…”

Section: A Study Regionmentioning

confidence: 99%

A Comparative Analysis Using Machine Learning Approach for Thunderstorm Prediction in Southern Region of Peninsular Malaysia

Rufus,

Ahmad,

Abdullah

et al. 2023

2023 International Symposium on Lightning Protection (XVII SIPDA)

View full text Add to dashboard Cite

Thunderstorms are one of the most destructive natural phenomena on the planet, as they are predominantly associated with lightning and heavy rainfall that result in human deaths, urban flooding, and agricultural damage. Thus, accurate thunderstorm prediction is essential for planning and managing agriculture, flood control, and air traffic control. This study utilized historical lightning and meteorological data from 2011 to 2018 in the southern regions of Peninsular Malaysia to predict thunderstorm occurrences. The lightning dataset is classified into three class ranges, where the high range of lightning rarely occurs in this region compared to the low and medium ranges of lightning because of the non-linear and complex characteristics of the thunderstorm and lightning itself, leading to an imbalanced dataset. The k-fold and stratified cross-validation (CV) methods and a resampling technique called SMOTE are introduced to overcome the imbalance in the training dataset. Then the dataset is trained and tested using five Machine Learning (ML) algorithms, including Decision Trees (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB). The results haveshown that the GB ML model using stratified k-fold CV and SMOTE is the best algorithm for thunderstorm prediction for this region, with accuracy ranging from 74% to 95%, recall ranging from 72% to 93%, precision ranging from 76% to 97%, and F1-Score ranging from 74% to 95%. Future thunderstorm predictions based on lightning patterns and meteorological datasets are expected to establish an early strategy to address the presence of thunderstorms by notifying the relevant authorities, to prevent any damage that may be caused by the thunderstorms.

show abstract

Section: A Study Regionmentioning

confidence: 99%