Lake Level Prediction using Feed Forward and Recurrent Neural Networks
Lake Level Prediction using Feed Forward and Recurrent Neural Networks
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DOI：
10.1007/s11269-019-02255-2
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发表时间：
2019-05-01
影响因子：
4.3
通讯作者：
Bonacci, Ognjen
中科院分区：
文献类型：
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作者：
Hrnjica, Bahrudin;Bonacci, Ognjen
在全球气候变化的背景下，保护高质量的淡水资源是影响当地人口和经济发展的关键因素，具有极其重要的意义。其中一个这样的淡水来源是Vrana湖，它位于克罗地亚完全岩溶化的Cres岛上。在过去的几十年里，湖泊水位的严重和危险的下降已经被记录下来。为了建立可靠的湖泊水位预测方法，首次将人工神经网络（ANN）应用于湖泊水位预测。这篇论文提出了一个时间序列预测模型，该模型基于过去38年里湖泊水位的月度测量，能够预测未来6个月或12个月。为了获得最佳的模型性能，采用两种类型的神经网络：长短期记忆（LSTM）递归神经网络（RNN）和前馈神经网络（FFNN）建立预测模型。该模型不是使用经典的滞后数据集，而是使用从时间序列数据中创建的不同长度的序列集来训练。使用相同的训练参数集对模型进行训练，为性能分析建立相同的条件。基于均方根误差（RMSE）和相关系数(R)的性能分析表明，两种模型类型都能获得满意的结果。分析还表明，无论模型类型如何，它们都优于基于具有固定数量特征和一个月预测周期的数据集的经典人工神经网络模型。分析还表明，该模型优于基于ARIMA和其他类似方法的经典时间序列预测模型。
The protection of high quality fresh water in times of global climate changes is of tremendous importance since it is the key factor of local demographic and economic development. One such fresh water source is Vrana Lake, located on the completely karstified Island of Cres in Croatia. Over the last few decades a severe and dangerous decrease of the lake level has been documented. In order to develop a reliable lake level prediction, the application of the artificial neural networks (ANN) was used for the first time. The paper proposes time-series forecasting models based on the monthly measurements of the lake level during the last 38 years, capable to predict 6 or 12 months ahead. In order to gain the best possible model performance, the forecasting models were built using two types of ANN: the Long-Short Term Memory (LSTM) recurrent neural network (RNN), and the feed forward neural network (FFNN). Instead of classic lagged data set, the proposed models were trained with the set of sequences with different length created from the time series data. The models were trained with the same set of the training parameters in order to establish the same conditions for the performance analysis. Based on root mean squared error (RMSE) and correlation coefficient (R) the performance analysis shown that both model types can achieve satisfactory results. The analysis also revealed that regardless of the model types, they outperform classic ANN models based on datasets with fixed number of features and one month the prediction period. Analysis also revealed that the proposed models outperform classic time series forecasting models based on ARIMA and other similar methods .