Intelligent Design Model of Urban Landscape Space Based on Optimized BP Neural Network

Abstract

Urban landscape space planning is an important application field of landscape ecology. With the continuous development of the field of architectural optimization, more and more optimization methods have sprung up, including various intelligent optimization algorithms. Such intelligent optimization algorithms usually rely on traditional building performance simulation methods to obtain building performance indicators for optimization in the optimization process. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of the building model and the configuration of the computer, which leads to too long performance optimization time for designers in the project. With efficient and accurate feedback, building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to invest in actual projects. Because the traditional BP neural network has its own limitations and its insufficient sample size and weak generalization ability in complex prediction problems, this paper uses the learning algorithm of optimizing the BP neural network to propose an urban landscape space intelligent design model. This article introduces the artificial neural network, a new application technology with the assistance of a geographic information system, and establishes a BP neural network model for urban landscape ecological planning. Seven elements of distance and number of residential points are used as input variables, patch density, fractal dimension, the Shannon diversity index, and aggregation degree are selected as output changes, and 20 samples are carefully collected to train the network. The results show that the network convergence effect is ideal and the generalization ability is strong, which provides a new simulation analysis method for landscape ecological planning.

Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

“…e clustering of variables affecting the sample function is generally referred to as variable Q-type cluster analysis, while the clustering of variables affecting the sample variables is referred to as function variable R-type cluster analysis [21].…”

“…. ., 24; first, the whole sample data set is clustered on the A1 dimensional group using the clustering method to obtain three classes of K m,n PSO (1,4,7,10,13,16,19,22), (2, 5, 8, 11, 14, 17 20, 23), and (3,6,9,12,15,18,21,24), and then apply the same method to cluster (1,4,7,10,13,16,19,22) first in A2 dimension to obtain one class (1,4,7,10,13,16,19,22) and cluster (2,5,8,11,14,17,20,23) in A2 dimension to obtain two classes (2,5,8,11,14,17) and (20,23), and one class (3,…”

Location Selection of Digital Cultural Tourism Town Based on Improved Genetic Algorithm and BP Neural Network

“…Taking Morden as an example, they believe that the positioning of small towns needs to consider a variety of factors, such as regional advantages, mature characteristic industries and cultural and creative industries, and local policy guidance. e authors (Vukovi ć Predrag, Č Avlin Gordana, and Č Avlin Miroslav) in various works proposed that the success of both rural tourism town in the UK benefits from natural hot spring resources, location conditions, and convenient transportation [16][17][18][19]. When studying the STP theory, (Daan Francois Toerien) found that the cultural and tourism towns have achieved good benefits in the innovative use of local characteristic resources [23,24].…”

“…In the next decade, operation oriented, resource integration, and fine management will become a new model of cultural tourism real estate. erefore, from the future positioning, it can be seen that the cultural tourism town urgently needs to clarify its own positioning, so as to lay a good foundation for better tourism integration in the future [16].…”

Urban Planning and Design Layout Generation Based on Artificial Intelligence

“…Today, in a digital era, computer technology is widely used in various elds [14][15][16][17][18][19], including architecture and urban areas. Concepts are parametric architectural design [20,21], computational urban design [22,23], urban big data analysis [24,25], and smart city emerge [26] one after another.…”