Prediction of areas of Oncomelania hupensis snail spread in Anhui Province based on five machine learning models
10.16250/j.32.1915.2024085
- VernacularTitle:基于5种机器学习模型的安徽省钉螺扩散面积预测研究
- Author:
Fenghua GAO
1
Author Information
1. Anhui Provincial Center for Disease Control and Prevention, Hefei, Anhui 230601, China
- Publication Type:Journal Article
- Keywords:
Oncomelania hupensis;
Machine learning;
Predictive efficiency;
Support vector regression model;
Nonlinear autoregressive neural network;
Back propagation neural network;
Gated recurrent unit neural network;
Long short-term memory neural network;
Anhui Province
- From:
Chinese Journal of Schistosomiasis Control
2024;36(6):572-576
- CountryChina
- Language:Chinese
-
Abstract:
Objective To predict the areas of Oncomelania hupensis snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of O. hupensis snail spread, so as to provide insights into investigating the trends in areas of O. hupensis snail spread. Methods Data pertaining to O. hupensis snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2). Following model training, the areas of O. hupensis snail spread were predicted in Anhui Province from 2024 to 2030. Results The cumulative areas of O. hupensis snail spread were 40 241.32 hm2 in Anhui Province from 1977 to 2023, and the area of O. hupensis snail spread varied greatly among years, with a periodic peak every 4 to 6 years. The fitting curves of SVR, NAR neural network, BP neural network, GRU neural network and LSTM neural network models were increasingly closer to the real curves for areas of O. hupensis snail spread in Anhui Province. The trends in areas of O. hupensis snail spread in Anhui Province from 2024 to 2030 appeared approximately "M"-shaped curves by SVR and NAR neural network models, approximately "W"-shaped curves by BP and GRU neural network models, and a unimodal conical curve by the LSTM neural network model. The LSTM neural network model had the best effect for predicting areas of O. hupensis snail spread in Anhui Province, with the RMSE of 1 277 480, MAE of 797 422 and R2 of 0.978 9, respectively. Conclusions Among the five models, The LSTM neural network model has a high efficiency for predicting areas of O. hupensis snail spread in Anhui Province, which may serve as a tool to investigate the trends in areas of O. hupensis snail spread.
