Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

Taillandier, Patrick; Duchêne, Cécile; Drogoul, Alexis

doi:10.1016/j.asoc.2010.11.012

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…The general way to determine the optimal number of hidden layer nodes is cut and trial method. Varying the number using a cut and trial method can be useful to optimize the number of hidden layer nodes [50]. The estimated value was used as the initial value of the cut and trial method, which was obtained by Equations (6)-(9) respectively.…”

Section: Back Propagation Artificial Neural Networkmentioning

confidence: 99%

An Integrated Indicator System and Evaluation Model for Regional Sustainable Development

Shi

Yuan

et al. 2019

Sustainability

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Regional sustainable development has become a worldwide issue in recent years, but there is no single and universally agreed method of choosing indicators for sustainable development assessment. The subjective selection of indicators will affect the results of assessment. Each evaluation method has its own advantages and disadvantages, and the methods used to determine indicator weight also differ. Regional sustainable development is a complex system, which is difficult to evaluate objectively and scientifically using a single method. Therefore, a new integrated indicator system and evaluation model is constructed here to more accurately reflect regional sustainable development level. The indicator system and evaluation model were constructed using a case study of 17 cities in Shandong Province, China. The indicator system includes 4 subsystems, i.e., economy, society, resource, and environment. These indicators were selected through correlation analysis and discrimination analysis. A back propagation neural network was applied to evaluate the respective scores of the 4 subsystems. The comprehensive score for regional sustainable development was evaluated using the analytic hierarchy process with entropy correction. The results show that sustainable development levels in these 17 cities show a gradually decreasing trend from east to west and from coast to inland. Cities with an underdeveloped economy usually display poor levels of social development and serious environmental pollution. Through the improvement of indicator screening, evaluation model, and result correction, the error caused by a single evaluation method can be reduced significantly. This new methodology for indicator selection and comprehensive evaluation provides a new perspective for the assessment of regional sustainable development.

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Section: Back Propagation Artificial Neural Networkmentioning

confidence: 99%

An Integrated Indicator System and Evaluation Model for Regional Sustainable Development

Shi

Yuan

et al. 2019

Sustainability

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“…• K Revised : is a revised version of the knowledge set defined by the AGENT model expert. The knowledge set was revised off-line with the approach proposed by Taillandier (Taillandier, et al, 2011). The results obtained with this knowledge set are good both in terms of efficiency and in terms of effectiveness.…”

Section: Case Study Contextmentioning

confidence: 99%

Multi-criteria diagnosis of control knowledge for cartographic generalisation

Taillandier

2012

European Journal of Operational Research

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International audienceThe development of interactive map websites increases the need of efficient automatic cartographic generalisation. The generalisation process, which aims at decreasing the level of details of geographic data in order to produce a map at a given scale, is extremely complex. A classical method for automating the generalisation process consists in using a heuristic tree-search strategy. This type of strategy requires having high quality control knowledge (heuristics) to guide the search for the optimal solution. Unfortunately, this control knowledge is rarely perfect and its evaluation is often difficult. Yet, this evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to offer an automatic method for evaluating the quality of control knowledge for cartographic generalisation based on a heuristic tree-search strategy. Our diagnosis method consists in analysing the system's execution logs, and in using a multi-criteria analysis method for evaluating the knowledge global quality. We present an industrial application as a case study using this method for building block generalisation and this experiment shows promising results

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Level of Details Harmonization Operations in OpenStreetMap Based Large Scale Maps

Touya

Baley

2017

Citizen Empowered Mapping

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OpenStreetMap data comprise of very detailed (e.g. zebra crossing) and quite rough features (e.g. built-up area). But making large scale maps from data with inconsistent level of detail often blurs map comprehension. This paper explores the automatic harmonization of OpenStreetMap data for large scale maps, i.e. the process that transforms rough objects to make them consistent with detailed objects. A typology of the new operators that harmonization requires is presented and six algorithms that implement the operators are described. Experiments with these algorithms raise several research questions about automation, parametrization, or the level of abstraction of the transformation, which are discussed in the paper.

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Automatic revision of the control knowledge used by trial and error methods: Application to cartographic generalisation

Cited by 6 publications

References 31 publications

An Integrated Indicator System and Evaluation Model for Regional Sustainable Development

An Integrated Indicator System and Evaluation Model for Regional Sustainable Development

Multi-criteria diagnosis of control knowledge for cartographic generalisation

Level of Details Harmonization Operations in OpenStreetMap Based Large Scale Maps

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