Mechanistic models are considerably more robust as predictive tools than are purely empirical models . 机械模型作为预测工具，比纯粹的经验模型要完善得多。
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An artificial neural network （ ann ） model was developed and used in different water bodies to predict timing for environmental changes as well as for the dynamics of resources . the results show that the ann model is superior to classical statistical models ( csm ) and can be used as predictive tool for highly non - linear phenomena 用人工神经网络方法对不同水域、不同环境因子之间非线性和不确定性的复杂关系进行学习训练并预测检验，结果表明:人工神经网络方法在模拟和预测方面均优于传统的统计回归模型，在资源与环境方面的应用是可行的，具有较强的模拟预测能力。
