Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction

Rashid, Rabiatul A. A.; Nohuddin, Puteri N. E.; Zainol, Zuraini

doi:10.1007/978-3-319-70010-6_12

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…To detect livelihood vulnerability patterns, data mining process was used. The process aims at nding valid, useful, novel and understandable patterns in database (Rashid et al, 2017). Accordingly, Cross Industrial Standard Process for Data Mining (CRISP-DM) that is the most widely-used analytics model was implemented in 6 steps as follows: 1) problem understanding in the form of detecting livelihood vulnerability patterns applying the sub-components, 2) data understanding in terms of acceptable range, missing data, outliers, and data consistency, 3) data preparation in the form of converting to binary data for association rules model, 4) modeling in the form of association rules to specify the relations among the considered sub-components, 5) evaluation in the form of adjusting results to address the detection of livelihood vulnerability patterns, 6) deployment in the form of offering patterns to policy makers to reduce pastoralists' livelihood vulnerability to climate change.…”

Section: Detecting Livelihood Vulnerability Patternsmentioning

confidence: 99%

Compatibility of climate adaptation strategies with livelihood vulnerability patterns: the case of Fars province, Iran

Ghazali,

Zibaei,

Skataric

et al. 2023

Local Environment

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Pastoralists' livelihood on natural rangelands are constantly in uenced by the destructive impacts of climate change. While the phenomenon of climate change continues on a widespread scale, it is expected to put more pressure on unfavorable rangelands and pastoral households whose livelihood source is only based on pastoralism activity. Thus, it is vital to reduce livelihood vulnerability of pastoralists to climate change impacts through appropriate adaptation strategies. Accordingly, the aim of the current study was to specify the compatibility of adaptation strategies adopted by pastoralists with their livelihood vulnerability patterns. The evaluation was based on a spatial survey that was conducted with a random sample of 393 Iranian pastoralists distributed in the counties of Eghlid, Sepidan, and Shiraz in the northern Fars province. The results showed that pastoralists were commonly vulnerable in terms of the high-risk exposure and low adaptive capacity. About 27 livelihood vulnerability patterns were detected using data mining based on the relationships among the sub-components of exposure, sensitivity, and adaptation in 3 counties. The scores of the livelihood vulnerability were high in most of the livelihood vulnerability patterns. Only 4 patterns obtained the relatively low vulnerability scores through accurate decisions on adopting the appropriate set of adaptation strategies. According to the low compatibility of adaptation strategies with the corresponding vulnerability patterns, the results of this study can assist pastoralists to adopt appropriate adaptation strategies and thus, decrease livelihood vulnerability to climate change. In this regard, it is suggested that pastoralists in each vulnerability pattern adopt appropriate adaptation strategies according to their vulnerability sub-components.

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Section: Detecting Livelihood Vulnerability Patternsmentioning

confidence: 99%

Compatibility of climate adaptation strategies with livelihood vulnerability patterns: the case of Fars province, Iran

Ghazali,

Zibaei,

Skataric

et al. 2023

Local Environment

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“…These techniques provide both technical and theoretical support to prevent as well as manage air pollution (Li et al, 2019). Association rule mining has also been used in terms of monitoring weather behavioral data to develop a prediction model for climate variability (Rashid et al, 2017). Furthermore, climate variability has an impact on agriculture, which demands a greater understanding with regard to the impact of the climate on crop production and food security.…”

Section: Data Analysis Tasks Of Climate Change Researchesmentioning

confidence: 99%

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Sebestyén

Czvetkó

Abonyi

2021

Front. Environ. Sci.

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The aim of this paper is to provide an overview of the interrelationship between data science and climate studies, as well as describes how sustainability climate issues can be managed using the Big Data tools. Climate-related Big Data articles are analyzed and categorized, which revealed the increasing number of applications of data-driven solutions in specific areas, however, broad integrative analyses are gaining less of a focus. Our major objective is to highlight the potential in the System of Systems (SoS) theorem, as the synergies between diverse disciplines and research ideas must be explored to gain a comprehensive overview of the issue. Data and systems science enables a large amount of heterogeneous data to be integrated and simulation models developed, while considering socio-environmental interrelations in parallel. The improved knowledge integration offered by the System of Systems thinking or climate computing has been demonstrated by analysing the possible inter-linkages of the latest Big Data application papers. The analysis highlights how data and models focusing on the specific areas of sustainability can be bridged to study the complex problems of climate change.

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“…In addition, this method is still popular and been used recently to solve the problem in various domain [13]- [15]. Association rules are usually required to satisfy a userspecified minimum support and a user-specified minimum confidence at the same time [16].…”

Section: B Cyber Love Fraud Pattern Recognition In Malaysia Using Apmentioning

confidence: 99%

Cyber Romance Scam Victimization Analysis using Routine Activity Theory Versus Apriori Algorithm

Saad¹,

Abdullah²,

Murah³

2018

ijacsa

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The advance new digital era nowadays has led to the increasing cases of cyber romance scam in Malaysia. These technologies have offered both opportunities and challenge, depending on the purpose of the user. To face this challenge, the key factors that influence the susceptibility to cyber romance scam need to be identified. Therefore, this study proposed cyber romance scam models using statistical method and Apriori techniques to explore the key factors of cyber romance scam victimization based on the real police report lodged by the victims. The relationship between demographic variables such as age, education level, marital status, monthly income and independent variables such as level of computer skills and the level of cyber-fraud awareness has been investigated. Then, the result of this study was compared with Routine Activity Theory (RAT). This study found that those between the ages of 25 and 45 years were likely to be the victims of cyber romance scams in Malaysia. The majority of the victims are educated and having a Diploma. In addition, this research shows that married people are more likely to be the victims of cyber romance scams. Study shows that non-income individuals are also vulnerable to being the victims because the study shows that 17 percent of respondents who are the victims are from this group. As expected, those who work and have monthly income between RM2001 and above are more likely to be targeted and become a victim of cyber romance scams. The study also shows that those who lack computer skills and less levels of cyber-fraud awareness are more likely to be victims of cyber romance scams.

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Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction

Cited by 10 publications

References 15 publications

Compatibility of climate adaptation strategies with livelihood vulnerability patterns: the case of Fars province, Iran

Compatibility of climate adaptation strategies with livelihood vulnerability patterns: the case of Fars province, Iran

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

Cyber Romance Scam Victimization Analysis using Routine Activity Theory Versus Apriori Algorithm

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