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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Vydané v:	Scientific reports Ročník 15; číslo 1; s. 36087 - 22
Hlavní autori:	Chen, Xiaolong, Wong, Cora Un In, Zhang, Hongfeng, Song, Zhengchun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 15.10.2025 Nature Publishing Group Nature Portfolio
Predmet:	639/705 639/705/258 639/705/531 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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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-20007-8