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首页> 外文会议>International Conference on Estuaries and Coasts(ICEC-2006); 20061128-30; Guangzhou(CN) >SALINE TIDE PREDICTION MODEL STUDY OF THE PEARL RIVER BAYOU BASED ON BP NEURAL NETWORK
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SALINE TIDE PREDICTION MODEL STUDY OF THE PEARL RIVER BAYOU BASED ON BP NEURAL NETWORK

机译：基于BP神经网络的珠江湾咸潮预报模型研究。

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摘要

Accurate Prediction of saline tide is the strong technical support for making and implementing the emergency plans of preventing saline tide, and for water resources management of the Pearl River basin. So, the study of the forecast model for Pearl River Bayou is very important. In this paper, the BP artificial neural network(ANN) is applied to study the saline tide forecast model of the Pearl River Bayou. The adoptive function is sigmoid function which is of strong non-linear mapping advantage. This model is able to simulate and forecast the complicate mechanism of saline tide, to identify the movement principle of saline tide and forecast the process of the saline tide along the Pearl River Delta. Experiments show that the forecast process of the saline tide by the ANN model is very well.
机译：准确预测盐水潮汐，是制定和实施预防盐水潮灾应急预案，珠江流域水资源管理的有力技术支撑。因此，研究珠江口河的预报模型非常重要。本文运用BP人工神经网络（ANN）研究了珠江河口的潮汐预报模型。代管函数是S型函数，具有强大的非线性映射优势。该模型能够模拟和预测生理盐水潮汐的复杂机制，识别生理盐水潮汐的运动原理，并预测生理盐水沿珠江三角洲的进程。实验表明，利用人工神经网络模型对咸潮的预报过程是很好的。

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