Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

Download Full-text

Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148088 ◽

2021 ◽

Vol 791 ◽

pp. 148088

Author(s):

Kunsen Lin ◽

Youcai Zhao ◽

Lu Tian ◽

Chunlong Zhao ◽

Meilan Zhang ◽

...

Keyword(s):

Neural Network ◽

Municipal Solid Waste ◽

Convolutional Neural Network ◽

Short Term Memory ◽

One Dimension ◽

Short Term ◽

Term Memory ◽

Mechanism Model ◽

Long Short Term Memory

Download Full-text

Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

Sustainability ◽

10.3390/su13010104 ◽

2020 ◽

Vol 13 (1) ◽

pp. 104

Author(s):

Dana-Mihaela Petroșanu ◽

Alexandru Pîrjan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Short Term Memory ◽

Electricity Consumption ◽

Short Term ◽

Term Memory ◽

Storage Room ◽

Long Short Term Memory ◽

Commercial Center ◽

Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

Download Full-text