Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.

中文翻译：

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Zrebar湖（伊朗）的每日水位预测：M5P，随机森林，随机树和减少错误修剪树算法之间的比较

Zrebar湖是伊朗最大的淡水湖之一，在环境生态系统中发挥着重要作用，而其干燥对周围的生态系统则具有负面影响。尽管如此，从生态旅游的角度来看，这个湖泊还是一个有趣的休闲场所。通过简单而实用的方法对湖泊水位进行预测和预报，可以为今后的湖泊水资源管理提供可靠的工具。在本研究中，我们通过基于决策树的著名算法（包括M5修剪（M5P），随机森林（RF），随机树（RT）和减少错误的修剪树）预测伊朗Zrebar湖的每日水位。 （重复）。我们使用了五种不同的水输入组合来找到最有效的一种。对于我们的建模，我们选择了70％的数据集进行训练（2011年至2015年）和30％的模型评估（2015年至2017年）。我们使用不同的定量评估（均方根误差（RMSE），平均绝对误差（MAE），确定系数（R²），百分比偏差（PBIAS）和均方根误差与测量数据的标准偏差（RSR）的比率）和视觉框架（泰勒图和箱形图）。我们的结果表明，滞后时间为一天的水位对结果的影响最大，而通过增加滞后时间，其对结果的影响会减小。结果表明，所有开发的模型均具有良好的预测能力，但M5P模型的性能优于其他模型，其次是RF和RT，然后是REPT。我们的结果表明，这些算法仅以一天的水位滞后时间作为输入就可以准确地预测水位，并且它们是未来预测的经济有效的工具。