Flood Prediction using ARIMA Model in Sungai Melaka, Malaysia

Wong, Wei Ming

doi:10.30534/ijatcse/2020/160942020

Cited by 4 publications

(2 citation statements)

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“…The EWMA model's prediction outcomes are smoother and more accurate than the MA model's. In FWMS, the Autoregressive Integrated Moving Average (ARIMA) model is widely used [8,9,10]. The data difference was used to filter the non-stationary components in the original sequence and yield better prediction results.…”

Section: Literature Reviewmentioning

confidence: 99%

Moving-update Kalman Algorithm in Low-cost Node-Red IoT Network for Estimating Flood Water Level

Nguyen¹,

Le²,

Le³

et al. 2022

J. ADV. ENG. COMPUT.

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Flooding is one of the most common natural disasters in Vietnam. Although a hydrological monitoring system has been developed in Vietnam, the adoption of a Flood Warning and Monitoring System (FWMS) is still limited. A practical issue is that the river water levels is rarely flat, but undulating with flood water ripples, which makes the measurement inaccurate. In this paper, we will design a recursive Kalman estimation for fluctuating flood water level in the Node-Red IoT network. Indeed, the low complexity of the popular Kalman filter algorithm is very suitable for a low-cost IoT system like Node-Red. In our experiments, the accuracy of our Kalman algorithm is far superior to the standard Moving Average (MA) algorithm. To our knowledge, this is the first time that the Kalman filter has been used in a practical Node-Red IoT experiment. We will show that our novel Moving-update Kalman algorithm, which combines MA and Kalman methods, can track data recursively without prior knowledge of noise’s variance. Our novel algorithm is of linear complexity and, hence, fast enough for low cost IoT and FWMS systems in developing countries like Vietnam. We also included the industrial Message Queuing Telemetry Transport (MQTT) protocol in IoT network in our Node-Red system, which means our designed Node-Red proposal is capable of transferring data to any FWMS network via internet. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Section: Literature Reviewmentioning

confidence: 99%

Moving-update Kalman Algorithm in Low-cost Node-Red IoT Network for Estimating Flood Water Level

Nguyen¹,

Le²,

Le³

et al. 2022

J. ADV. ENG. COMPUT.

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“…The results show that ARIMA (1,3,1) is the best selected model due to its significant p-value. Flood prediction for Pengkalan Rama, Melaka river using ARIMA model was studied by Wong et al (2020). The best ARIMA model was identified by the parameter Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Dengue Outbreak Data Using ARIMA Model

Ali¹,

Pazil²,

Mahmud³

et al. 2021

IJARBSS

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Dengue fever is an internationally recognised virus that is spread by mosquitoes and can result in death. From recorded cases, Selangor has the highest rate of dengue infection among Malaysian populations. Corona disease , a new pandemic that has swept the globe, like Selangor, has prompted this report on the pattern of dengue cases during COVID-19 pandemic. Due to the new outbreak of COVID-19, the Movement Control Order (MCO) has been extended from time to time, and with most health resources at the state and federal levels being used to combat COVID-19, dengue control activities have been limited to a noncontact activity in outbreaks and hotspot areas. The importance of this study is to investigate the increase in dengue cases in Selangor because Selangor recorded the highest number of cases in Malaysia. Considering that there are not many studies conducted in Selangor, this study is important to predict dengue cases, and the authorities can take immediate action to overcome this problem. The aim of this research is to find the best ARIMA model for predicting the dengue cases in Selangor in the future. Several ARIMA models were used to test dengue cases data obtained in Selangor in order to achieve the objectives. The best model was calculated by comparing the Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) measurement errors. Then, the predicted number of dengue cases was calculated using the best model generated. The model for which the values of criteria are the smallest is considered as the best model. Hence ARIMA (1,1,2) was found to be the best model for predicting the dengue cases data series and this model is used to predict the number of dengue cases in Selangor with the smallest Mean Square Error (MSE) value of 12837.4327 and Root Mean Square Error (RMSE) value of 113.3024. The forecasted values showed a decreasing number of dengue cases. This study was carried out using Rstudio software and excel. Further research can be conducted using another time series method, for example Holt-winters method.

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