Prediction and Visualization of the Disaster Risks in the Philippines Using Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN)

Alquisola, Gillian Lindsay V.; Coronel, Daniel Jose A.; Reolope, Bryan Matthew F.; Roque, Julia Nicole A.; Acula, Donata D.

doi:10.1109/ccoms.2018.8463238

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Four methods were experimented such as Decision Trees (DT), Support Vector Machine (SVM), Adaptive Boosting Algorithm with Decision Trees as Base Estimator (AdaBoost-DT), and Adaptive Boosting Algorithm with Support Vector Machine as Base Estimator (AdaBoost-SVM). The methods were also compared in the accuracy of the previous research [7] where DWT-ARIMA-ANN was used. This research concluded that Decision Trees outperformed the remaining models.…”

Section: Discussionmentioning

confidence: 99%

“…This knowledge will lead the policymakers to allocate more resources to the prone areas to reduce casualties during the phenomena. The same study was conducted by Alquisola et al [7] in 2018 that predicts the disaster risks using various models such as Autoregressive Integrated Moving Average (ARIMA), Discrete Wavelet Transform (DWT), and Artificial Neural Networks (ANN). The combined algorithms generated an accuracy of 68% for casualties, 39.84% for Damaged Houses, and 33.33% for Damaged Properties.…”

Section: Classification Of Disaster Risks In the Philippines Using Adaptive Boosting Algorithm With Decision Trees And Support Vector Macmentioning

confidence: 99%

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Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

Acula

2021

J. Mod. Sim. Mater.

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This paper employed the intelligent approach based on machine learning categorized as base and ensemble methods in classifying the disaster risk in the Philippines. It focused on the Decision Trees, Support Vector Machine, Adaptive Boosting Algorithm with Decision Trees, and Support Vector Machine as base estimators. The research used the Exponential Regression for missing value imputation and converted the number of casualties, damaged houses, and properties into five (5) risk levels using Quantile Method. The 10-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Decision Trees and Adaptive Decision Trees are the most suitable models for the disaster data with the score of more than 90%, more than 75%, more than 75% in all the classification metrics (accuracy, precision, recall f1-score) when applied to classification risk levels of casualties, damaged houses and damaged properties respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Of Disaster Risks In the Philippines Using Adaptive Boosting Algorithm With Decision Trees And Support Vector Macmentioning

confidence: 99%

Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

Acula

2021

J. Mod. Sim. Mater.

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“…The researcher in this study is the focus to improve the output of the hybrid model. This model uses ARIMA and ANN with DWT for disaster risk prediction in the Philippines provinces in terms of casualties [Alquisola et al 2018]. The existing tool for data classification is mostly used manual data input and prepared data.…”

Section: Related Workmentioning

confidence: 99%

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

Dubey

Katarya

2021

Preprint

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Disaster is a very serious dissipation that arises for a short time period, but the impact of that disaster on human society is very dangerous and very long-lasting. Disasters are categorized into two types like natural disasters and manmade disasters. Among all disasters, of all the natural disasters, flood is the commonplace natural disaster. Flood disaster that causes huge loss of human life, diversity as well as economic loss, which is very dangerous for the developing countries and developed countries also. Nowadays during the monsoon season flood is dangerous for all the geographical areas located nearby water bodies. Much research has been done for flood detection. Machine Learning and many other recent technologies are playing a vital role in predicting the occurrence of floods. For prediction purposes, a huge amount of data is requiring collected from sensors deployed in various locations. In this paper, we used the Batch normalization with Deep Neural Network (BNDNN) technique for the classification of data in three classes as Low, Moderate, and High. The result obtained from our proposed model is compared with some other models like Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). In this our proposed BNDNN provides 89% accuracy which is higher among all existing models. Models are compared based on some parameters like Accuracy, Precision, Recall, F –Score. The compression among all the models used in this paper shows that our proposed model provides better results.

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“…In the last step, we portray in Fig. 12 our seasonal ARIMA time series model to forecast future values (Alquisola et al 2018).…”

Section: Forecasting Visualizationmentioning

confidence: 99%

Application of machine learning time series analysis for prediction COVID-19 pandemic

Chaurasia

Pal

2020

Res. Biomed. Eng.

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Purpose Coronavirus disease is an irresistible infection caused by the respiratory disease coronavirus 2 (SARS-CoV-2). It was first found in Wuhan, China, in December 2019, and has since spread universally, causing a constant pandemic. On June 3, 2020, 6.37 million cases were found in 188 countries and regions. During pandemic prevention, this can minimize the impact of the disease on individuals and groups. A study was carried out on coronavirus to observe the number of cases, deaths, and recovery cases worldwide within a specific time period of 5 months. Based on this data, this research paper will predict the future spread of this infectious disease in human society. Methods In our study, the dataset was taken from WHO "Data WHO Coronavirus Covid-19 cases and deaths-WHO-COVID-19-global-data". This dataset contains information about the observation date, provenance/state, country/region, and latest updates. In this article, we implemented several forecasting techniques: naive method, simple average, moving average, single exponential smoothing, Holt linear trend method, Holt-Winters method and ARIMA, for comparison, and how these methods improve the Root mean square error score. Results The naive method is best suited as described over all other methods. In the ARIMA model, utilizing grid search, we recognized a lot of boundaries that delivered the best-fit model for our time series data. By continuing the model, future predictions of death cases indicate that the number of deaths will increased by more than 600,000 by January 2021. Conclusion This survey will support the government and experts in making arrangements for what is about to happen. Based on the findings of instantaneous model, these models can be adjusted to guide long time.

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Prediction and Visualization of the Disaster Risks in the Philippines Using Discrete Wavelet Transform (DWT), Autoregressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN)

Cited by 10 publications

References 2 publications

Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

Classification of Disaster Risks in the Philippines using Adaptive Boosting Algorithm with Decision Trees and Support Vector Machine as Based Estimators

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

Application of machine learning time series analysis for prediction COVID-19 pandemic

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