Buku ini merupakan uraian untuk memudahkan pemahaman konsep, tingkat dasar sampai lanjut dalam sistem cerdas dan penerapannya melalui pemanfaatan teknologi Big Data, dengan mengedepankan keterampilan dalam pembuatan dan hasil implementasi dengan berbagai kombinasi algoritma berbasis sistem cerdas maupun dengan perpaduan berbagai macam tools untuk membangun ekosistem analisis Big Data yang powerfull.
An improved entropy-cluster algorithm approach was used to develop the flood risk map.
Abstract Floods are considered as one of the most frequently occurring natural hazards worldwide and are occurring increasingly frequent in recent decades. Flood risk assessment is an important tool for flood prevention and involves significant practical applications in flood risk management and flood disaster reduction.
In the study, an integrated methodology is proposed by incorporating urban flood inundation model, improved entropy weight method and k-means cluster algorithm to evaluate urban flood risk.
The proposed approach is data driven without considering classification standard of different risk levels, and thus provides a more reasonable and objective result. A region in Haikou, China is adopted to test the applicability of the proposed approach.
Seven evaluation indices are selected by coupling the natural hazard index system and hydrological models. The index weights are calculated by an improved entropy weight method that integrates the entropy weight method and analytic hierarchy process AHP method.
Subsequently, the k-means cluster algorithm is used to develop the flood risk map in the study area.
The results indicate that high risk zones cover The assessment result matches well with the historical data of flood events. The traditional cluster algorithm and technique for order of preference by similarity to ideal solution TOPSIS methods are used for comparison with the improved entropy-cluster algorithm so as to validate the proposed approach for risk management.
The result demonstrates that the proposed approach is feasible and exhibits the most reasonable classification result. The study outcomes provide a novel approach for flood risk assessment and can provide valuable information for urban flood management.The paper discusses the traditional K-means algorithm with Basic K-means Algorithm: A centroid-based Clustering technique The research has been done on clustering algorithms that improves accuracy of the results with better accuracy lesser time complexity.
But there are still many departments that needs to be. The paper discusses the traditional K-means algorithm with advantages and disadvantages of it. It also includes researched on enhanced k-means proposed by various authors and it also includes the techniques to improve.
In addition to the paper presentation, Drini will also be showing the technique at UIST’s popular demo session.
Our second contribution is the interactive projection mapping system for PaintsChainer that we showed at the Winter Comiket last year. For those of you who missed it, ColourAIze (which is how we call it in the paper) works directly with drawings and art on paper.
Research Paper On K-Means Algorithm. Click on any of the term papers to read a brief synopsis of the research paper.
The essay synopsis includes the number of pages and sources cited in the paper. Theme for English By Langston Hughes. Research papers. Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China.
Author links open overlay panel Hongshi Xu a b Chao Ma a b Jijian Lian a b Kui Xu a b Evance Chaima a b. Show more. An Efficient k-Means Clustering Algorithm: Analysis and Implementation Tapas Kanungo, Senior Member, IEEE, David M. Mount,Member, IEEE, Nathan S. .