Abstract

This paper presents the results of predicting nutrients in rivers on national level by the use of two artificial intelligence methodologies. Artificial neural network (ANN) and support vector machine (SVM) were used to predict annual concentration of nitrate and phosphate in rivers of eleven European countries. For creation of an optimal model of prediction, 23 industrial, economical and agricultural parameters were used for the period from 2000 to 2011. The data from 2000 to 2010 was used for training, while the data for 2011 was used for model validation. Optimization of different parameters of ANN and SVM was conducted in order to obtain the model with the best performances. Results of created models were evaluated by using statistical performances indicator named coefficient of determination (R²). The obtained results showed that ANN has better results in predicting nitrate and phosphate compared to SVM models. These results suggest that ANN model is a promising tool for prediction of nutrients in rivers.

摘要

本文介绍了使用两种人工智能方法在国家层面预测河流养分的结果。人工神经网络（ANN）和支持向量机（SVM）被用来预测11个欧洲国家河流中硝酸盐和磷酸盐的年浓度。为了建立最佳预测模型，使用了 2000 年至 2011 年期间的 23 个工业、经济和农业参数。2000 年至 2010 年的数据用于训练，而 2011 年的数据用于模型验证。对 ANN 和 SVM 的不同参数进行优化，以获得具有最佳性能的模型。通过使用称为决定系数的统计性能指标（R ²）。所得结果表明，与 SVM 模型相比，ANN 在预测硝酸盐和磷酸盐方面具有更好的结果。这些结果表明 ANN 模型是预测河流养分的有前途的工具。