Open AccessStream order selection for model generalization of the topographic map of Indonesia
Author(s) -
Fahrul Hidayat,
Nugroho Purwono,
Danang Budi Susetyo,
Mochamad Irwan Hariyono,
Tia Rizka,
Nuzula Rachma,
Rizka Windiastuti
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/500/1/012022
Subject(s) - generalization , cartographic generalization , similarity (geometry) , scale (ratio) , representation (politics) , computer science , selection (genetic algorithm) , topographic map (neuroanatomy) , data mining , feature (linguistics) , value stream mapping , algorithm , pattern recognition (psychology) , artificial intelligence , mathematics , image (mathematics) , geography , cartography , engineering , philosophy , law , cognitive psychology , mathematical analysis , linguistics , posterior parietal cortex , psychology , political science , politics , operations management , lean manufacturing
The importance of stream networks is related to other features in the topographic map e.g. as weighted-parameter for contour derivation. The generalization of these features needs complex parameters specifically geometrical and conceptual aspects. Geometric parameters consist of stream length and vertices, while the conceptual part handles stream networks connectivity as logical consequences. Stream networks selection is a type of important step on map features analysis and in map databases. This paper proposes a new approach for stream networks generalization of Topographic Map of Indonesia (as known as RBI) for 1:5,000 to 1:25,000 of scale by using geometrical and conceptual parameters. Three stages used in this research were: data pre-processing (include resolving the topological errors), generating stream order (1:5,000 of scale as an input), comparing stream order algorithms (Strahler, Scheidegger, Shreve, and Drwal), and performing feature similarity-based analysis (comparison of stream ordering results and 1:25,000 of scale). The research resulted four different stream orders and eight different similarity-analysis values since each algorithm was tested in two scenarios (in 1 st scenario, order > 1 were selected while in 2 nd scenario, order > 2 were selected). Eventually, after comparing those results, the Scheidegger method obtained the highest similarity value in on both 1 st and 2 nd scenarios. Further, generalization by using stream order selection delivered the representation of river in constructing map elements of RBI.