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A coastline generalization method that considers buffer consistency.

ABSTRACT: Coastlines are the boundary between the ocean and land and are an important line feature in spatial data. Coastlines must be adapted via map generalization when they are stored in multi-scale spatial databases. This article presents a new method of coastline generalization that considers the buffer consistency from the original coastline to the generalized coastlines. This method uses the geographical distance field to identify the feature points and bends that influence the buffer consistency from the original coastline to the generalized coastline, and the process is also used to simplify the bends and maintain the concave and convex characteristics in the generalization. This method is compared to the bend-simplification method, and the results indicate that the proposed method can preserve the shape characteristics of coastlines. Moreover, the seaward buffer boundaries from the original coastline to the generalized coastline are consistent when the distance is greater than the tolerance of the coastline generalization.

SUBMITTER: Yang H

PROVIDER: S-EPMC6211722 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A coastline generalization method that considers buffer consistency.

Yang Hui H Li Lin L Hu Hai H Wu Yanlan Y Xia Hui H Liu Yu Y Tan Shudong S

PloS one 20181101 11

Coastlines are the boundary between the ocean and land and are an important line feature in spatial data. Coastlines must be adapted via map generalization when they are stored in multi-scale spatial databases. This article presents a new method of coastline generalization that considers the buffer consistency from the original coastline to the generalized coastlines. This method uses the geographical distance field to identify the feature points and bends that influence the buffer consistency f ...[more]

PMID: 30383812

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Project description:We describe the generalized weighted thermodynamic perturbation (gwTP) method for estimating the free energy surface of an expensive "high-level" potential energy function from the umbrella sampling performed with multiple inexpensive "low-level" reference potentials. The gwTP method is a generalization of the weighted thermodynamic perturbation (wTP) method developed by Li and co-workers [J. Chem. Theory Comput. 2018, 14, 5583-5596] that uses a single "low-level" reference potential. The gwTP method offers new possibilities in model design whereby the sampling generated from several low-level potentials may be combined (e.g., specific reaction parameter models that might have variable accuracy at different stages of a multistep reaction). The gwTP method is especially well suited for use with machine learning potentials (MLPs) that are trained against computationally expensive ab initio quantum mechanical/molecular mechanical (QM/MM) energies and forces using active learning procedures that naturally produce multiple distinct neural network potentials. Simulations can be performed with greater sampling using the fast MLPs and then corrected to the ab initio level using gwTP. The capabilities of the gwTP method are demonstrated by creating reference potentials based on the MNDO/d and DFTB2/MIO semiempirical models supplemented with the "range-corrected deep potential" (DPRc). The DPRc parameters are trained to ab initio QM/MM data, and the potentials are used to calculate the free energy surface of stepwise mechanisms for nonenzymatic RNA 2'-O-transesterification model reactions. The extended sampling made possible by the reference potentials allows one to identify unequilibrated portions of the simulations that are not always evident from the short time scale commonly used with ab initio QM/MM potentials. We show that the reference potential approach can yield more accurate ab initio free energy predictions than the wTP method or what can be reasonably afforded from explicit ab initio QM/MM sampling.

Dataset Information

A coastline generalization method that considers buffer consistency.

Publications

A coastline generalization method that considers buffer consistency.

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