scholarly journals Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning

2019 ◽  
Vol 11 (11) ◽  
pp. 1265 ◽  
Author(s):  
Li Kuang ◽  
Xuejin Yan ◽  
Xianhan Tan ◽  
Shuqi Li ◽  
Xiaoxian Yang
Keyword(s):  
Short Term Memory ◽  
Historical Data ◽  
Temporal Correlation ◽  
Time Interval ◽  
Short Term ◽  
Prediction Task ◽  
Task Learning ◽  
Spatiotemporal Feature ◽  
The Common ◽  
Long Short Term Memory

Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models.

Deep learning reservoir porosity prediction based on multilayer long short-term memory network

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA213-WA225
Author(s):  
Wei Chen ◽  
Liuqing Yang ◽  
Bei Zha ◽  
Mi Zhang ◽  
Yangkang Chen
Keyword(s):  
Oil And Gas ◽  
High Efficiency ◽  
Short Term Memory ◽  
Southern China ◽  
Prediction Errors ◽  
Short Term ◽  
Drilling Depth ◽  
Term Memory ◽  
Prediction Task ◽  
Long Short Term Memory

The cost of obtaining a complete porosity value using traditional coring methods is relatively high, and as the drilling depth increases, the difficulty of obtaining the porosity value also increases. Nowadays, the prediction of fine reservoir parameters for oil and gas exploration is becoming more and more important. Therefore, high-efficiency and low-cost prediction of porosity based on logging data is necessary. We have developed a machine-learning method based on the traditional long short-term memory (LSTM) model, called multilayer LSTM (MLSTM), to perform the porosity prediction task. We used three different wells in a block in southern China for the prediction task, including a training well and two test wells. One test well has the same logging data type as the training well, whereas the other test well differs from the training well in the logging depth and parameter types. Two different types of test data sets are used to detect the generalization ability of the network. A set of data was used to train the MLSTM network, and the hyperparameters of the network were adjusted through experimental accuracy feedback. We also tested the performance of the network using two sets of log data from different regions, including generalization and sensitivity of the network. During the training phase of the porosity prediction model, the developed MLSTM establishes a minimized objective function, uses the Adam optimization algorithm to update the weight of the network, and adjusts the network hyperparameters to select the best target according to the feedback of the network accuracy. Compared with conventional sequence neural networks, such as the gated recurrent unit and recurrent neural network, the logging data experiments show that MLSTM has better robustness and accuracy in depth sequence prediction. Especially, the porosity value at the depth inflection point can be better predicted when the trend of the depth sequence was predicted. This framework is expected to reduce the porosity prediction errors when data are insufficient and log depths are different.

Electric Load Forecasting with Deep Machine Learning

2019 ◽  
Vol 16 (8) ◽  
pp. 3404-3409
Author(s):  
Ala Adin Baha Eldin Mustafa Abdelaziz ◽  
Ka Fei Thang ◽  
Jacqueline Lukose
Keyword(s):  
Mobile Application ◽  
Energy Demand ◽  
Short Term Memory ◽  
Historical Data ◽  
Electrical Energy ◽  
Training Data ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Term Memory ◽  
Long Short Term Memory

The most commonly used form of energy in houses, factories, buildings and agriculture is the electrical energy, however, in recent years, there has been an increase in electrical energy demand due to technology advancements and rise in population, therefore an appropriated forecasting system must be developed to predict these demands as accurately as possible. For this purpose, five models were selected, they are Bidirectional-Long Short Term Memory (Bi-LSTM), Feed Forward Neural Network (FFNN), Long Short Term Memory (LSTM), Nonlinear Auto Regressive network with eXogenous inputs (NARX) and Multiple Linear Regression (MLR). This paper will demonstrate the development of these selected models using MATLAB and an android mobile application, which is used to visualize and interact with the data. The performance of the selected models was evaluated by performing the Mean Absolute Percent Error (MAPE), the selected historical data used to perform the MAPE was obtained from Toronto, Canada and Tasmania, Australia, where the year 2006 until 2016 was used as training data and the year 2017 was used to test the MAPE of the historical data with the models’ data. It is observed that the NARX model had the least MAPE for both the regions resulting in 1.9% for Toronto, Canada and 2.9% for Tasmania, Australia. Google cloud is used as the IoT (Internet of Things) platform for NARX data model, the 2017 datasets is converted to JavaScript Object Notation (JSON) file using JavaScript programming language, for data visualization and analysis for the android mobile application.

scholarly journals A Two-Stage Short-Term Load Forecasting Method Using Long Short-Term Memory and Multilayer Perceptron

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5873
Author(s):  
Yuhong Xie ◽  
Yuzuru Ueda ◽  
Masakazu Sugiyama
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Historical Data ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory

Load forecasting is an essential task in the operation management of a power system. Electric power companies utilize short-term load forecasting (STLF) technology to make reasonable power generation plans. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators. In recent years, machine learning has become one of the most popular technologies for load forecasting. In this paper, a two-stage STLF model based on long short-term memory (LSTM) and multilayer perceptron (MLP), which improves the forecasting accuracy over the entire time horizon, is proposed. In the first stage, a sequence-to-sequence (seq2seq) architecture, which can handle a multi-sequence of input to extract more features of historical data than that of single sequence, is used to make multistep predictions. In the second stage, the MLP is used for residual modification by perceiving other information that the LSTM cannot. To construct the model, we collected the electrical load, calendar, and meteorological records of Kanto region in Japan for four years. Unlike other LSTM-based hybrid architectures, the proposed model uses two independent neural networks instead of making the neural network deeper by concatenating a series of LSTM cells and convolutional neural networks (CNNs). Therefore, the proposed model is easy to be trained and more interpretable. The seq2seq module performs well in the first few hours of the predictions. The MLP inherits the advantage of the seq2seq module and improves the results by feeding artificially selected features both from historical data and information of the target day. Compared to the LSTM-AM model and single MLP model, the mean absolute percentage error (MAPE) of the proposed model decreases from 2.82% and 2.65% to 2%, respectively. The results demonstrate that the MLP helps improve the prediction accuracy of seq2seq module and the proposed model achieves better performance than other popular models. In addition, this paper also reveals the reason why the MLP achieves the improvement.

Semantic Segmentation of QRS Complex in Single Channel ECG with Bidirectional LSTM Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 758-762 ◽  
Author(s):  
Lingfeng Liu ◽  
Baodan Bai ◽  
Xinrong Chen ◽  
Qin Xia
Keyword(s):  
Short Term Memory ◽  
Single Channel ◽  
Semantic Segmentation ◽  
Time Interval ◽  
Qrs Complex ◽  
Short Term ◽  
Attention Model ◽  
Interval Prediction ◽  
Long Short Term Memory ◽  
Electrocardiogram Ecg

In this paper, bidirectional Long Short-Term Memory (BiLSTM) networks are designed to realize the semantic segmentation of QRS complex in single channel electrocardiogram (ECG) for the tasks of R peak detection and heart rate estimation. Three types of seq2seq BiLSTM networks are introduced, including the densely connected BiLSTM with attention model, the BiLSTM U-Net, and the BiLSTM U-Net++. To alleviate the sparse problem of the QRS labels, symmetric label expansion is applied by extending the single R peak into a time interval of fixed length. Linear ensemble method is introduced that averages the outputs of different BiLSTM networks. The cross-validation results show significant increase of the accuracy and decrease of discontinuous gaps in the QRS interval prediction by the ensembling over singular neural networks.

scholarly journals A Multivariate Long Short-Term Memory Neural Network for Coalbed Methane Production Forecasting

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2045
Author(s):  
Xijie Xu ◽  
Xiaoping Rui ◽  
Yonglei Fan ◽  
Tian Yu ◽  
Yiwen Ju
Keyword(s):  
Neural Network ◽  
Coalbed Methane ◽  
Short Term Memory ◽  
Historical Data ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Long Short Term Memory ◽  
Cbm Production ◽  
Coalbed Methane Production

Owing to the importance of coalbed methane (CBM) as a source of energy, it is necessary to predict its future production. However, the production process of CBM is the result of the interaction of many factors, making it difficult to perform accurate simulations through mathematical models. We must therefore rely on the historical data of CBM production to understand its inherent features and predict its future performance. The objective of this paper is to establish a deep learning prediction method for coalbed methane production without considering complex geological factors. In this paper, we propose a multivariate long short-term memory neural network (M-LSTM NN) model to predict CBM production. We tested the performance of this model using the production data of CBM wells in the Panhe Demonstration Area in the Qinshui Basin of China. The production of different CBM wells has similar characteristics in time. We can use the symmetric similarity of the data to transfer the model to the production forecasting of different CBM wells. Our results demonstrate that the M-LSTM NN model, utilizing the historical yield data of CBM as well as other auxiliary information such as casing pressures, water production levels, and bottom hole temperatures (including the highest and lowest temperatures), can predict CBM production successfully while obtaining a mean absolute percentage error (MAPE) of 0.91%. This is an improvement when compared with the traditional LSTM NN model, which has an MAPE of 1.14%. In addition to this, we conducted multi-step predictions at a daily and monthly scale and obtained similar results. It should be noted that with an increase in time lag, the prediction performance became less accurate. At the daily level, the MAPE value increased from 0.24% to 2.09% over 10 successive days. The predictions on the monthly scale also saw an increase in the MAPE value from 2.68% to 5.95% over three months. This tendency suggests that long-term forecasts are more difficult than short-term ones, and more historical data are required to produce more accurate results.

scholarly journals Application of Long Short Term Memory Networks for Long- and Short-term Bus Travel Time Prediction

2021 ◽  
Author(s):  
Osama Osman ◽  
Hesham Rakha ◽  
Archak Mittal
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Short Term Memory ◽  
Temporal Correlation ◽  
Travel Time Prediction ◽  
Short Term ◽  
Term Memory ◽  
Time Prediction ◽  
Long Short Term Memory ◽  
Control Devices

This study introduces a comparative analysis of two deep learning (multilayer perceptron neural networks (MLP-NN) and the long short term memory networks (LSTMN)) models for transit travel time prediction. The two models were trained and tested using one-year worth of data for a bus route in Blacksburg, Virginia. In this study, the travel time was predicted between each two successive stations to all the model to be extended to include bus dwell times. Additionally, two additional models were developed for each category (MLP of LSTM): one for only segments including controlled intersections (controlled segments) and another for segments with no control devices along them (uncontrolled segments). The results show that the LSTM models outperform the MLP models with a RMSE of 17.69 sec compared to 18.81 sec. When splitting the data into controlled and uncontrolled segments, the RMSE values reduced to 17.33 sec for the controlled segments and 4.28 sec for the uncontrolled segments when applying the LSTM model. Whereas, the RMSE values were 19.39 sec for the controlled segments and 4.67 sec for the uncontrolled segments when applying the MLP model. These results demonstrate that the uncertainty in traffic conditions introduced by traffic control devices has a significant impact on travel time predictions. Nonetheless, the results demonstrate that the LSTMN is a promising tool that can has the ability to account for the temporal correlation within the data. The developed models are also promising tools for reasonable travel time predictions in transit applications.

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