Long Short-Term Memory Networks for Real-Time Runoff Forecasting using Remotely Sensed Data

2021 ◽  
Author(s):  
Paul Muñoz ◽  
David F. Muñoz ◽  
Johanna Orellana-Alvear ◽  
Hamed Moftakhari ◽  
Hamid Moradkhani ◽  
...  
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Data Representation ◽  
Tropical Andes ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory ◽  
Hydrological Applications

<p>Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been explored in hydrological applications such as runoff forecasting. An aditional challenge to the forecasting task arises from the uncertainties generated when using readily-available Remote Sensing (RS) imagery aimed to overcome lack of in-situ data describing the runoff governing processes. Here, we proposed a runoff forecasting framework for a 300-km<sup>2 </sup>mountain catchment located in the tropical Andes of Ecuador. The framework consists on real-time data acquisition, preprocessing and runoff forecasting for lead times between 1 and 12 hours. LSTM models were fed with 18 years of hourly runoff, and precipitation data from the novel PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now) product. Model efficiencies according to the NSE metric ranged from 0.959 to 0.554, for the 1- to 12-hour models, respectively. Considering that the concentration time of the catchment is approximately 4 hours, the proposed framework becomes a useful tool for delivering runoff forecasts to decision makers, stakeholders and the public. This study has shown the suitability of using the PDIR-Now product in a LSTM-modeling framework for real-time hydrological applications. Future endeavors must focus on improving data representation and data assimilation through feature engineering strategies.</p><p><strong>Keywords: </strong>Long Short-Term Memory; PDIR-Now; Hydroinformatics; Runoff forecasting; Tropical Andes</p>

scholarly journals Daily Runoff Forecasting Using a Cascade Long Short-Term Memory Model that Considers Different Variables

2021 ◽  
Vol 35 (4) ◽  
pp. 1167-1181
Author(s):  
Yun Bai ◽  
Nejc Bezak ◽  
Bo Zeng ◽  
Chuan Li ◽  
Klaudija Sapač ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory

Decoding Movements from Cortical Ensemble Activity Using a Long Short-Term Memory Recurrent Network

2019 ◽  
Vol 31 (6) ◽  
pp. 1085-1113 ◽  
Author(s):  
Po-He Tseng ◽  
Núria Armengol Urpi ◽  
Mikhail Lebedev ◽  
Miguel Nicolelis
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Unscented Kalman Filter ◽  
Bipedal Walking ◽  
Motor Tasks ◽  
Short Term ◽  
Term Memory ◽  
Cortical Areas ◽  
Long Short Term Memory ◽  
Ensemble Activity

Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( N = 134–402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.

The importance of short lag-time in the runoff forecasting model based on long short-term memory

2020 ◽  
Vol 589 ◽  
pp. 125359
Author(s):  
Xi Chen ◽  
Jiaxu Huang ◽  
Zhen Han ◽  
Hongkai Gao ◽  
Min Liu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Lag Time ◽  
Forecasting Model ◽  
Short Term ◽  
Term Memory ◽  
Model Based ◽  
Runoff Forecasting ◽  
Long Short Term Memory

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Abdullah Alharbi ◽  
Wael Alosaimi ◽  
Radhya Sahal ◽  
Hager Saleh
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Real Time ◽  
Short Term Memory ◽  
Stream Processing ◽  
Care Providers ◽  
Short Term ◽  
Term Memory ◽  
Risk Of Death ◽  
Long Short Term Memory

Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart’s function to discover its irregularity to detect the health problems early. Rapid technological advancement (e.g., artificial intelligence and stream processing technologies) allows healthcare sectors to consolidate and analyze massive health-based data to discover risks by making more accurate predictions. Therefore, this work proposes a real-time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time-series dataset is extracted from Medical Information Mart for Intensive Care (MIMIC-II). Recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BI-LSTM) are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance.

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