An intelligent decision-making system for flood monitoring from space

Vladimirova, Tanya; Yuhaniz, Siti Sophiayati

doi:10.1007/s00500-009-0514-1

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Usually, such expert systems are helpful where the events under investigation change rapidly and their prediction cannot be made in advance. Flooding can be considered as an example of such event, which has the highest capability to bring sufferings to the human beings and therefore, its assessment of risk is very important (Hossain and Davies 2001Vladimirova and Yuhaniz 2011;Gnecco et al 2016). Hence, wireless sensor network technologies have been used to collect flood-related data, and eventually, they are fed into Decision Support Systems (DSSs) to generate different decision scenarios and to predict flooding in an area Hossain 2014, 2015;González et al 2013;Demeritt et al 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel anomaly detection algorithm for sensor data under uncertainty

2016

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It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, Communicated by imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications.

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Section: Introductionmentioning

confidence: 99%