ANFIS模型在河流流量预测中的比较分析——以塞汉盆地为例

F. Özkan, B. Haznedar
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引用次数: 1

摘要

为了使人类的生活没有问题,需要对现有水资源的保护和利用进行合理的规划。未来水源的潜力应作为这种规划的第一步加以确定。因此,河流流量预报对于提供与水系运行有关的各种问题的基本信息是必要的。本文对土耳其Seyhan盆地的zamantyRiver-Değirmenocağı、zamantyRiver-Ergenuşağı和Eğlence River-Eğribük站的长期日流量进行了研究。为了从过去的流量测量值预测前向流量,采用反向传播(BP)、混合学习(HB)和模拟退火(SA)算法对自适应神经模糊推理系统(ANFIS)模型进行了训练,并对结果进行了比较。使用网格划分(GP)和模糊c均值聚类(FCM)方法创建不同输入参数的ANFIS模型的性能也进行了测试。比较的评价标准为平均绝对误差(MAE)、均方根误差(RMSE)、决定系数(R2)和平均绝对百分比误差(MAPE)。采用BP模型对FMSs的R2值分别为0.6854、0.9242和0.9373,结果最佳。分析结果表明,BP算法比其他算法更能成功有效地训练非线性问题中的ANFIS参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of ANFIS models in Prediction of Streamflow: the case of Seyhan Basin
In order to sustain human life without problems, a rational planning is required for the conservation and use of existing water resources. The potential of future water sources should be determined as the first step in such planning. Therefore, river flow forecasting is necessary to provide basic information about a variety of problems related to the operation of river systems. In this study, the long-term daily flow values of the Zamantı River-Değirmenocağı, Zamantı River-Ergenuşağı, and Eğlence River-Eğribük stations in the Seyhan Basin in Turkey were examined. In order to predict the forward flow rate from past flow measurement values, the Adaptative Neuro-Fuzzy Inference System (ANFIS) model was trained using Backpropagation (BP), Hybrid Learning (HB), and Simulated Annealing (SA) algorithms, and the results were compared. The performance of ANFIS models created with different input parameters using Grid Partitioning (GP) and Fuzzy C-Means Clustering (FCM) methods was also examined. The evaluation criteria used for comparison were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Determination Coefficient (R2), and Mean Absolute Percentage Error (MAPE). The best results for R2 values of 0.6854, 0.9242, and 0.9373 were obtained for FMSs using the BP model. As a result of the analysis, it was concluded that the BP algorithm could be used more successfully and effectively than other algorithms for training ANFIS parameters in nonlinear problems.
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