ENBQA: An Ensemble Learning-Based Model for Beach Quality Assessment

The assessment of beach quality is an important prerequisite for beach development and serves as the foundation for coastal zone management and sustainable development. This topic has attracted widespread attention, and various evaluation systems have been established. Given that beach quality assessment (BQA) involves multidimensional and nonlinear indicators, machine learning methods are well-suited to handling complex data relationships. However, current research utilizing machine learning for BQA often faces challenges such as limited evaluation indicators and difficulties in obtaining relevant data. in this study, a machine learning-based model for beach quality evaluation is proposed to address the limitations of existing evaluation frameworks, particularly under conditions of data scarcity. Simulated data were generated, and the analytic hierarchy process was integrated to extract features from 21 beach evaluation factors. A comparative analysis was conducted using the following four machine learning models: decision tree, random forest, XGBoost, and MLP. Results indicate that XGBoost (mean squared error (MSE)=0.1825, weighted F1=0.7513) and MLP (Pearson coefficient=0.6053) outperform traditional models. Furthermore, an ensemble learning model combining XGBoost and MLP was developed, substantially improving predictive performance (reducing MSE to 0.0753, increasing the Pearson coefficient to 0.8002, and achieving an F1 score of 0.783). Validation using real data from Yangkou Beach demonstrated that the model maintained an accuracy of 58% even when 5–10 evaluation factors had randomly missing values.