Comparative Analysis of Long Short-Term Memory and Storage Function Model for Flood Water Level Forecasting of Bokha Stream in NamHan River, Korea

2022 ◽  
pp. 127415
Author(s):  
Donghyun Kim ◽  
Joonseok Lee ◽  
Jongsung Kim ◽  
Myungjin Lee ◽  
Wonjoon Wang ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Water Level ◽  
Short Term Memory ◽  
Short Term ◽  
Function Model ◽  
Term Memory ◽  
Flood Water ◽  
Storage Function ◽  
Long Short Term Memory ◽  
And Storage

scholarly journals Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1389 ◽  
Author(s):  
Chen Liang ◽  
Hongqing Li ◽  
Mingjun Lei ◽  
and Qingyun Du
Keyword(s):  
Water Level ◽  
Lake Water ◽  
Short Term Memory ◽  
Dongting Lake ◽  
Three Gorges Dam ◽  
Three Gorges ◽  
Short Term ◽  
Term Memory ◽  
Lake Water Level ◽  
Long Short Term Memory

To study the Dongting Lake water level variation and its relationship with the upstream Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory (LSTM) network is used to establish a model that predicts the daily water levels of Dongting Lake. Seven factors are used as the input for the LSTM model and eight years of daily data (from 2003 to 2012) are used to train the model. Then, the model is applied to the test dataset (from 2011 to 2013) for forecasting and is evaluated using the root mean squared error (RMSE) and the coefficient of determination (R2). The test shows the LSTM model has better accuracy compared to the support vector machine (SVM) model. Furthermore, the model is adjusted to simulate the situation where the TGD does not exist to explore the dam’s impact. The experiment shows that the water level of Dongting Lake drops conspicuously every year from September to November during the TGD impounding period, and the water level increases mildly during dry seasons due to TGD replenishment. Additionally, the impact of the TGD results in a water level decline in Dongting Lake during flood peaks and a subsequent lagged rise. This research provides a tool for flood forecasting and offers a reference for TGD water regulation.

scholarly journals Development and Assessment of Water-Level Prediction Models for Small Reservoirs Using a Deep Learning Algorithm

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 55
Author(s):  
Tsumugu Kusudo ◽  
Atsushi Yamamoto ◽  
Masaomi Kimura ◽  
Yutaka Matsuno
Keyword(s):  
Water Management ◽  
Deep Learning ◽  
Water Level ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Water Levels ◽  
Short Term ◽  
Term Memory ◽  
Deep Learning Algorithm ◽  
Long Short Term Memory

In this study, we aimed to develop and assess a hydrological model using a deep learning algorithm for improved water management. Single-output long short-term memory (LSTM SO) and encoder-decoder long short-term memory (LSTM ED) models were developed, and their performances were compared using different input variables. We used water-level and rainfall data from 2018 to 2020 in the Takayama Reservoir (Nara Prefecture, Japan) to train, test, and assess both models. The root-mean-squared error and Nash–Sutcliffe efficiency were estimated to compare the model performances. The results showed that the LSTM ED model had better accuracy. Analysis of water levels and water-level changes presented better results than the analysis of water levels. However, the accuracy of the model was significantly lower when predicting water levels outside the range of the training datasets. Within this range, the developed model could be used for water management to reduce the risk of downstream flooding, while ensuring sufficient water storage for irrigation, because of its ability to determine an appropriate amount of water for release from the reservoir before rainfall events.

scholarly journals Comparative analysis of short-term demand predicting models using ARIMA and deep learning

2021 ◽  
Vol 11 (4) ◽  
pp. 3319
Author(s):  
Halima Bousqaoui ◽  
Ilham Slimani ◽  
Said Achchab
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Convolutional Neural Network ◽  
Demand Uncertainty ◽  
Short Term Memory ◽  
Real Life ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The forecasting consists of taking historical data as inputs then using them to predict future observations, thus determining future trends. Demand prediction is a crucial component in the supply chain’s process that allows each member to enhance its performance and its profit. Nevertheless, because of demand uncertainty supply chains usually suffer from many problems such as the bullwhip effect. As a solution to those logistics issues, this paper presents a comparative analysis of four time series demand forecasting models; namely, the autoregressive integrated moving Average (ARIMA) a statistical model, the multi-layer perceptron (MLP) a feedforward neural network, the long short-term memory model (LSTM) a recurrent neural network and the convolutional neural network (CNN or ConvNet) a deep learning model. The experimentations are carried out using a real-life dataset provided by a supermarket in Morocco. The results clearly show that the convolutional neural network gives slightly better forecasting results than the Long short-term memory network.

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