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999			_c97106 _d97112
003			MY-KuUP
005			20251125110041.0
006			t||||fr|||| 000 0
008			220428s2021 my a|||frm||| 000 0 eng d
020			_aTHE0009353(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aKK .M33 2021 r Thesis
100	1		_aNurmadiha Osman, _eauthor.
245	1	0	_aEvaluation of arima and ann stream analytics for air quality monitoring system / _cNurmadiha Osman
264		1	_aKuantan, Pahang : _bUMP, _c2021
264		4	_c© 2021
300			_axv, 186 pages : _billustrations (some color) ; _c30 cm. + _e1 CD ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aCollege of Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2021
504			_aIncludes bibliographical references
520	3		_aThere are many environmental monitoring systems available in the market with Internetof-Things (IoT) enabled technology. However, the existing system is not equipped with online data analytics. Some of them provide analytics but are done in offline mode through third-party software or devices known as batch analytics. Pricewise, the existing monitoring system alone is expensive even though none of them are furnished with stream analytics. The thesis presents the design and development of an accurate air quality monitoring system equipped with streaming machine learning predictive analytics called Smart Environmental System (SES). The developed SES is divided into two sections End-Node Unit (ENU) and Gateway Unit (GWU). ENU consisted of calibrated sensors of NO2, CO, CO2, PM2.5, PM10, O3, temperature humidity integrated with Raspberry Pi Single-Board Computer (SBC) and Long-Range (LoRa) Transmitter (Tx) module. Meanwhile, GWU consisted of Raspberry Pi SBC, LoRa Receiver (Rx) and 4G module. The ENU transferred the data wirelessly to the GWU through LoRa communication, and GWU stored the data immediately in MySQL, which was installed in the Linux Apache MySQL PHP (LAMP) server. Investigation on evaluating senso rs’ accuracy is executed by comparing the collected data by SES vs data from the Department of Environment (DoE). The SES’s accuracy percentage error of CO, NO2, O3, PM10 are 5.1%, 7%, 6.1% and 6% correspondingly compared to DoE. Such accuracy of sensors is acceptable with an accuracy below 10%. Once accuracy has been validated, the data stored in MySQL database is successfully exported to the R query table in R-Server by using dbGetQuery() command, checked and aligned with the MySQL database. It is observed that the data in MySQL are successfully exported to the R query table based on the similar number of variables between those two tables. The data stored in the query table act as input to the analytics algorithm, which runs in R-server as well. In this thesis, two algorithms have been implemented and compared. Auto -Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). It is identified that ARIMA has better prediction accuracy (PA) percentage of 99.45%, 99.87%, 99.75%, 98.92% for CO, NO2, O3 and PM10 over ANN thus chosen as a predictive analytics algorithm for SES. Once embedded in SES, ARIMA performances are evaluated based on Mean Absolute Percentage Error (MAPE) and Prediction Accuracy (PA). It is observed that ARIMA MAPE is 1.64%,9.67%, 9.59%, 7.09%, for CO, NO2, O3 and PM10, respectively which led PA to achieve 96.78%, 90.33%, 90.41% and 92.91% correspondingly. The results proved that the proposed SES is able to precisely predict those gases for the next 24 hours above the 90% prediction accuracy. It can be concluded the proposed SES could be implemented as a future for the Air Pollutant Index (API) system.
610	2	0	_aCollege of Engineering _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS