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Evaluation of factors affecting turbidity in Dez dam reservoir using Decision tree forests and Group method of data handling

机译：利用决策树林和数据处理群法对德兹坝水库浊度影响的因素评价

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In this study, Group method of data handling (GMDH) and Decision tree forests (DTF) used to evaluate the effective parameters on turbidity on Dez dam reservoir. In this way, Decision tree forest, that combines multiple Decision tree, used to evaluate the relative importance of factors affecting turbidity. At the second step, based on effective parameters and by GMDH, will be presented a model to predict the turbidity values. A random study was conducted in the Dez dam reservoir from Jan 2002 to July 2003. Dez dam in Iran is facing a serious sedimentation problem, and its dead volume will be quite full in coming 10 years, and now the inflow water in the hydropower conduit system is becoming turbid. Based on results in this study, DTF and GMDH model could accurately evaluate relative importance of variables on turbidity and provide a reliable estimate to predict it.
机译：在本研究中，用于评估DEZ坝储层对浊度有效参数的数据处理（GMDH）和决策树林（DTF）的组方法。通过这种方式，结合多个决策树的决策树林用于评估影响浊度的因素的相对重要性。在第二步，基于有效参数和GMDH，将呈现一个模型以预测浊度值。在2002年1月至2003年1月，在德国坝水库进行了随机研究。伊朗的Dez大坝面临着严重的沉降问题，其未来10年的死亡量将是完全充分的，现在水电导管中的流入水分系统变得浑浊。基于本研究的结果，DTF和GMDH模型可以准确地评估变量对浊度的相对重要性，并提供可靠的估计来预测它。

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