Enhanced backpropagation neural network accuracy through an improved genetic algorithm for tourist flow prediction in an ecological village

Extant tourism studies on predicting tourist flow often adopt Backpropagation Neural Network (BP-NN) and Genetic Algorithm-Backpropagation Neural Network (GABP-NN). However, those models cannot well address the challenge of nonlinear complexity of tourists’ mobility, and fuzzy decision-making due to...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 36087 - 22
Hauptverfasser: Chen, Xiaolong, Wong, Cora Un In, Zhang, Hongfeng, Song, Zhengchun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 15.10.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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Accuracy
Adaptability
Adaptive optimization
Algorithms
Back propagation
BP neural network
Cultural heritage
Decision making
Ecological scenic area
Genetic algorithm
Genetic Algorithms
Holidays & special occasions
Humanities and Social Sciences
Humans
Innovations
Machine learning
multidisciplinary
Mutation
Neural networks
Neural Networks, Computer
Nonlinear forecasting
Optimization
Ourist flow prediction
Pandemics
Population genetics
Population studies
Resource management
Science
Science (multidisciplinary)
Seasonal variations
Statistical methods
Sustainable development
Sustainable tourism
Tourism
Tourism development
Tourism industry
Tourists
Trends
Weather
BP neural network
Nonlinear forecasting
Ourist flow prediction
Ecological scenic area
Genetic algorithm
Adaptive optimization
ISSN:2045-2322, 2045-2322
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Zusammenfassung:Extant tourism studies on predicting tourist flow often adopt Backpropagation Neural Network (BP-NN) and Genetic Algorithm-Backpropagation Neural Network (GABP-NN). However, those models cannot well address the challenge of nonlinear complexity of tourists’ mobility, and fuzzy decision-making due to abrupt urgencies and foul weather. The current study proposes “Adaptive Multi-population Genetic Algorithm Backpropagation (AMGA-BP)”, which features a novel double-layer ladder-structured chromosome design for simultaneous optimization of network structure and weights. Experimental results demonstrate the AMGA-BP model achieves superior performance with a Mean Absolute Percentage Error (MAPE) of 5.32% and coefficient of determination ( r² ) of 0.9869, significantly outperforming traditional BP (25.22% MAPE) and GA-BP (13.61% MAPE) models. The model maintains robust accuracy during peak seasons (6.00% MAPE) and adverse weather conditions (5.50% MAPE), while also surpassing LSTM (8.20% MAPE) and Random Forest (9.80% MAPE) approaches. This advancement provides tourism managers with more reliable tools for visitor flow prediction, particularly in ecological sensitive areas like Banliang Ancient Village, contributing to sustainable tourism development and effective resource management.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-20007-8