Optimized ANN-ABC for Thunderstorms Prediction

Yasen, Mais; Al-Jundi, Ruba Amjad Salameh; Al-Madi, Nailah

doi:10.1109/ictcs.2017.37

Cited by 5 publications

(1 citation statement)

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“…It may also be useful in further studies focused on extreme events prediction for various problems related to effective disaster management. The application of machine learning algorithms in a thunderstorm prediction brings with a new promise for forthcoming studies concerning both operational predictors and meteorological research that intend to examine observed and future variations in frequencies of severe extreme events (Yasen et al, 2017;Taszarek et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

et al. 2021

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Accurate thunderstorm frequency (TSF) prediction is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed a novel hybrid machine learning model through hybridization of data pre-processing Ensemble Empirical Mode Decomposition (EEMD) with two state-of-arts models namely artificial neural network (EEMD-ANN), support vector machine (EEMD-SVM) for TSF prediction at three categories of yearly frequencies over Bangladesh. We were demarcated the yearly TSF datasets into three categories for the period 1981-2016 recorded at 28 sites; high (March-June), moderate (July-October), and low (November-February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared 2 with classical ANN, SVM, Autoregressive Integrated Moving Average (ARIMA). EEMD-ANN and EEMD-SVM hybrid models showed 8.02%-22.48% higher performance precision in terms of root mean square error (RMSE) compared to other models at high, moderate and low-frequency categories. Eleven out of 21 input parameters were selected based on the Random Forest (RF) variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid model developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world.

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Section: Discussionmentioning

confidence: 99%