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首页> 外文期刊>Journal of Hydrology >Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN
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Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN

机译：空气温度日常湖面水温建模：极其随机树木（ERT）与Air2water，Mars，M5tree，RF和MLPNN

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摘要

Prediction of rivers and lakes water temperature plays an important role in hydrology, ecology, and water resources planning and management. Recently, machines learning approaches have been widely used for modelling water temperature, and the obtained results vary depending on the kind of models and the selections of the appropriates predictors. In the present paper, a new family of machines learning are proposed and compared to the famous air2stream model, using a large data set collected at 25 lakes in the northern part of Poland. The proposed models were: (i) the extremely randomized trees (ERT), (ii) the multivariate adaptive regression splines (MARS), (iii) the M5 Model tree (M5Tree), (iv) the random forest (RF), and (v) the multilayer perceptron neural network (MLPNN). The models were developed using the air temperature as input variables and the component of the Gregorian calendar (year, month and day) number. Results obtained were evaluated using several statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), correlation coefficient (R) and Nash-Sutcliffe efficiency coefficient (NSE). Obtained results reveals that the air2stream model outperformed all other machines learning models and worked best with high accuracy at all the 25 lakes, and none of the ERT, MARS, M5Tree, RF and MLPNN models was able to provides an improvement of the water temperature prediction compared to the air2stream.
机译：河流和湖泊水温预测在水文，生态和水资源规划和管理中起着重要作用。最近，机器学习方法已广泛用于建模水温，所获得的结果取决于拟议预测因子的模型和选择的类型。在本文中，使用在波兰北部25湖中收集的大型数据集，提出了一系列新的机器学习，并与着名的Air2Stream模型进行了比较。拟议的模型是：（i）非常随机的树木（ert），（ii）多变量自适应回归样条（MARS），（iii）M5模型树（M5Tree），（IV）随机森林（RF），以及（v）多层的Perceptron神经网络（MLPNN）。使用空气温度作为输入变量和格雷戈里亚日历（年，月和日）的组件开发了模型。使用若干统计指标评估所获得的结果：根均线误差（RMSE），平均绝对误差（MAE），相关系数（R）和NASH-SUTCLIFFE效率系数（NSE）。获得的结果表明，Air2Stream模型优于所有其他机器学习模型，并在所有25个湖泊中高精度工作，并且没有ERT，MARS，M5Tree，RF和MLPNN型号能够提供水温预测的提高与Air2stream相比。

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