scholarly journals Retraction Note to: Distribution of earthquake activity in mountain area based on big data and teaching of landscape design

2021 ◽  
Vol 14 (22) ◽  
Author(s):  
Juan You
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaozhou Yang ◽  
Fan Bai

In order to improve the effect of urban landscape design, this paper combines big data technology with digital technology. For scenes and solutions containing SDS paths, a processing method similar to photon graphs is used and added to the calculation of two-way optical path tracking. In the processing scene, this paper uses the two-way optical path tracking method to perform specular reflection or refraction from the subpath starting from the light source and then store information such as the light energy of the points on the diffuse reflection surface or the directional reflection surface. Moreover, this paper combines the actual needs of urban landscape design to construct an urban landscape design system based on big data technology and digital technology. Finally, this paper designs experiments to carry out urban landscape simulation and design effect evaluation. From the test results, it can be seen that the system designed in this paper basically meets the needs of urban landscape planning and design.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Huashan Zhan

In order to improve the accuracy and efficiency of performance evaluation, the interactive application of virtual reality and intelligent big data in landscape design is proposed. Clara algorithm is used to mine the performance evaluation index data of landscape simulation design. The performance evaluation index system of landscape simulation system is established based on the data mined. BP network is used to build a comprehensive evaluation model. The expert scoring method is the evaluation index system scoring, which is used as the input of BP network, and the expected output is a neuron. The value of the neuron represents the comprehensive performance evaluation value of the landscape simulation system. The experimental results show that the evaluation results of the research method are consistent with the expert evaluation results, with high accuracy; with the increasing number of systems, the evaluation efficiency of the research method is faster.


2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Ke Cao ◽  
Jing Xiao ◽  
Yan Wu

Urban landscape design as a contemporary art embodies postmodernist philosophical thinking, aesthetic thinking, and breaking the traditional concept of art, and it is a new way of creating and presenting art. Big data technology characterized by large scale, speed, variety, value, and uncertainty of data is used to achieve urban landscape design. In this article, during the research process, we strive to raise the revelation of the design layer rather than the brand new level of cross-fertilization and interaction between big data-driven discrete dynamic model and urban landscape design; we also reveal how the benefits of promoting urban development and harmonious life are achieved in the interactive expression of the urban landscape after the application of the big data-driven discrete dynamic model, which provides designers and related professionals with more detailed and novel design ideas at the theoretical level and makes the theory of big data-driven discrete dynamic models in landscape design interactive methods more enriched. Finally, this article puts forward its thinking and outlook on the design of the big data-driven discrete dynamic model in the interactivity of urban landscape design, hoping that artists will strengthen its functional and material design elements when creating performance. Moreover, more design means of emerging technologies of modern science and technology should be integrated so that modern urban landscape can achieve ordinary and uncommon benefits and promote the rapid development of the big data-driven discrete dynamic model in urban landscape design development.


2021 ◽  
pp. 004728752110405
Author(s):  
Jian-Wu Bi ◽  
Chunxiao Li ◽  
Hong Xu ◽  
Hui Li

Accurate forecasting of daily tourism demand is a meaningful and challenging task, but studies on this issue are scarce. To address this issue, multisource time series data, relating to past tourist volumes, web search information, daily weather conditions, and the dates of public holidays, are selected as the forecasting variables. To fully capture the relationship between these forecasting variables and actual tourism demand automatically, an ensemble of long short-term memory (LSTM) networks is proposed with a correlation-based predictor selection (CPS) algorithm. The effectiveness of the proposed method is verified in daily tourism demand forecasting for the Huangshan Mountain Area, benchmarked against 11 forecasting methods. This study contributes to the literature by (1) introducing the use of big data in daily tourism demand forecasting, (2) proposing an ensemble of LSTM networks for daily tourism demand forecasting, and (3) providing an effective predictor selection algorithm in ensemble learning.


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