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首页> 外文期刊>Journal of Hydrology >A comparative study of hydrodynamic model and expert system related models for prediction of total suspended solids concentrations in Apalachicola Bay
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A comparative study of hydrodynamic model and expert system related models for prediction of total suspended solids concentrations in Apalachicola Bay

机译：预测阿巴拉契科拉湾总悬浮固体浓度的水动力模型和专家系统相关模型的比较研究

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This paper presents the development of expert system related models using the concepts of Geno-Kalman Filtering (GKF) and fuzzy logic to predict the concentration of total suspended solids (TSS) with the input of wind speed at the locations of Cat Point and Dry Bar in Apalachicola Bay, USA. The TSS and wind speed data recorded from June 1, 2005 to July 30, 2005 are used for the present modeling study. Data set is divided into two parts for the model development. The June data are selected for calibration and the July data are for model verification. The predicted TSS concentrations from the Geno-Kalman Filtering and fuzzy logic models are compared with those from a hydrodynamic model and measured data reported in Liu and Huang (2009). The statistical values in terms of the mean squared error (MSE), the coefficient of efficiency (CE) and the chi-square (χ~2) parameter between the observed data and predicted results for the evaluation of each model's performance are presented. It is noted that both the hydrodynamic and Geno-Kalman Filtering models are capable of predicting TSS concentration with the trend of dynamic variation. Although the fuzzy logic and the Geno-Kalman Filtering models outperform the hydrodynamic model, the Geno-Kalman Filtering model is found to be able to produce the most accurate results at the studied locations. The fuzzy logic model tends to under-predict the TSS concentrations.
机译：本文介绍了利用Geno-Kalman滤波（GKF）和模糊逻辑的概念来开发与专家系统相关的模型，并通过在Cat Point和Dry Bar位置输入风速来预测总悬浮固体（TSS）的浓度在美国阿巴拉契科拉湾。 2005年6月1日至2005年7月30日记录的TSS和风速数据用于本模型研究。数据集分为两部分，用于模型开发。选择六月数据进行校准，选择七月数据进行模型验证。将来自Geno-Kalman滤波和模糊逻辑模型的预测TSS浓度与来自流体动力学模型的预测TSS浓度进行比较，并在Liu和Huang（2009）中报告了测量数据。提出了根据均方误差（MSE），效率系数（CE）和观测数据与预测结果之间的卡方（χ〜2）参数的统计值，用于评估每个模型的性能。注意，流体动力学模型和Geno-Kalman滤波模型都能够预测TSS浓度，并具有动态变化趋势。尽管模糊逻辑和Geno-Kalman滤波模型优于流体力学模型，但发现Geno-Kalman滤波模型能够在所研究的位置产生最准确的结果。模糊逻辑模型倾向于低估TSS浓度。

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