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首页> 外文期刊>Hydrology and Earth System Sciences >Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam
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Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam

机译：广义与非广义神经网络模型在阿斯旺大坝多导水流量预报中的应用

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摘要

Artificial neural networks (ANN) have been found efficient, particularly in problems where characteristics of the processes are stochastic and difficult to describe using explicit mathematical models. However, time series prediction based on ANN algorithms is fundamentally difficult and faces problems. One of the major shortcomings is the search for the optimal input pattern in order to enhance the forecasting capabilities for the output. The second challenge is the over-fitting problem during the training procedure and this occurs when ANN loses its generalization. In this research, autocorrelation and cross correlation analyses are suggested as a method for searching the optimal input pattern. On the other hand, two generalized methods namely, Regularized Neural Network (RNN) and Ensemble Neural Network (ENN) models are developed to overcome the drawbacks of classical ANN models. Using Generalized Neural Network (GNN) helped avoid over-fitting of training data which was observed as a limitation of classical ANN models. Real inflow data collected over the last 130 years at Lake Nasser was used to train, test and validate the proposed model. Results show that the proposed GNN model outperforms non-generalized neural network and conventional auto-regressive models and it could provide accurate inflow forecasting.
机译：已经发现人工神经网络（ANN）是有效的，特别是在过程特征随机且难以使用显式数学模型描述的问题中。然而，基于人工神经网络算法的时间序列预测从根本上来说是困难的，并且存在问题。主要缺点之一是寻求最佳输入模式，以增强输出的预测能力。第二个挑战是训练过程中的过度拟合问题，当ANN失去其概括性时就会发生。在这项研究中，建议使用自相关和互相关分析作为搜索最佳输入模式的方法。另一方面，开发了两种通用方法，即正则化神经网络（RNN）和集成神经网络（ENN）模型，以克服经典ANN模型的缺点。使用广义神经网络（GNN）有助于避免训练数据过度拟合，而这被认为是经典ANN模型的局限性。过去130年在纳赛尔湖收集的实际流入数据用于训练，测试和验证所提出的模型。结果表明，所提出的GNN模型优于非广义神经网络和传统的自回归模型，并且可以提供准确的流量预测。

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