Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Outliers appear as extreme values but often contain information that is very important so it needs to be examined first whether the data is still used or issued. Outlier detection is a hot topic for research. Improved new technologies and a variety of applications cause an increase in the need for outlier detection. The outlier method was successfully applied in various fields, such as education, economics, business, health, space, geology, and credit cards. This study aims to discuss the design of outlier detection methods using the KMeans Clustering method. This study uses four different types of identification tests, there are Extreme Standard Deviation (ESD), Shapiro-Wilk W Test, Dixon-Type Test, and Boxplot-Rule. The results showed that the design of outlier detection methods can be used to compare and find which is more effective in solving problems in the grouping of national exam absorption data in Mathematics. Grouping is done by using 40 indicators of competency achievement on 5 materials tested: Algebra, Geometry and Trigonometry, calculus, and Statistics. Further research can be carried out at the implementation and evaluation stages of the design of this outlier detection method
The use of rainfall data for flood early warning prediction have been highlighted by several researchers, in the last couple of decades. This study investigates the involvement of antecedent daily rainfall, for the determination of rainfall thresholds, to be used for flood early warning purposes at the upper watershed, Java island, Indonesia. An inventory of 70 flood occurrences for the period of 1992–2017 was compiled, and rainfall data were retrieved from 37 stations. First, calculate the critical discharge to determine the flood status in each watershed based on the results of statistical analysis of the frequency of the data series of discharge. Second, a procedure for the calculation of rainfall thresholds for flood occurrence was followed consisting of four steps: i) determining the rainfall associated with each inventory of flood occurrence and nonoccurrence; ii) the antecedent daily rainfall was calculated for 1 to 7 days for the selected dates and watersheds; iii) the optimum number of antecedent rainfall days was evaluated; and (iv) empirical rainfall thresholds for flood occurrence were determined. The results showed flood occurrences are best predicted using a combination of daily rainfall and 7 days of antecedent rainfall for all alert zones (A, B, C and D) including regional model (RRTM) with a negative relationship between antecedent rainfall and daily rainfall. Rainfall threshold models have an overall accuracy of more than 95%. It has provided evidence that the flood event in the study area is preceded by soil conditions that is saturated due to rain a few days before the flood
