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Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Haze alarm visual map (HazeViz): an intelligent haze forecaster Mohd Said Syukri Morsid; Syeril Azira Jamaluddin; Nur Azmina Hood; Norshahida Shaadan; Yap Bee Wah; Muthukkaruppan Annamalai
Bulletin of Electrical Engineering and Informatics Vol 8, No 1: March 2019
Publisher : Institute of Advanced Engineering and Science

The haze problem has intensified in recent years. The particulate matter of less than 10 microns in size, PM10 is the dominant air pollutant during haze. In this paper, we present the development of HazeViz, a Haze Alarm Visual Map forecaster, which is based on PM10. The intelligent web application allows users to visualize the pattern of PM10 in a region, forecasts PM10 value and alarms bad haze condition. HazeViz was developed using HTML, Java Script, PHP, MySQL, R Programming and Fusionex Giant. The SARIMA statistical forecasting models that underlie the application were developed using R. The PM10 trend analysis, and the consequential map and chart visualizations were implemented on the Fusionex GIANT Big Data Analytics platform. HazeViz was developed in the context of the Klang Valley, our case study. The dataset was obtained from Department of Environment Malaysia, which contains a total of 157,680 hourly PM10 data for six stations in Klang Valley, for the years 2013 to 2015. The SARIMA models were developed using maximum daily PM10 data for 2013 and 2014, and the 2015 data was used to validate the model. The fitting models were determined based on the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). While the selected models were implemented in HazeViz and successfully deployed on the web, the results show that the selected models have MAPE ranging between 35 percent and 45 percent, which implies that the models are still far from robust. Future work can consider augmented SARIMA models that can yield improved results.

Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data Nur Hanisah Abdul Malek; Wan Fairos Wan Yaacob; Yap Bee Wah; Syerina Azlin Md Nasir; Norshahida Shaadan; Sapto Wahyu Indratno
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp598-608

Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.

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