APPLICATION OF DEEP LEARNING TO LONG-TERM PREDICTION OF DAM INFLOW TOWARD EFFICIENCY OF THE FLOOD CONTROL OPERATION.

The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders’ and ship-owners’ decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.

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A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series

Future Transportation ◽

10.3390/futuretransp1030042 ◽

2021 ◽

Vol 1 (3) ◽

pp. 765-776

Author(s):

Jianqing Wu ◽

Bo Du ◽

Qiang Wu ◽

Jun Shen ◽

Luping Zhou ◽

...

Keyword(s):

Deep Learning ◽

Time Delay ◽

Dwell Time ◽

Short Term Memory ◽

Multivariate Time Series ◽

Learning Approaches ◽

Term Prediction ◽

Long Term Prediction ◽

Delay Prediction

In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.

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Long-Term Prediction of Some Officer-Effectiveness Measures from Aptitude Tests

PsycEXTRA Dataset ◽

10.1037/e594882009-001 ◽

1958 ◽

Author(s):

R. L. Thorndike ◽

Elizabeth P. Hagen

Keyword(s):

Aptitude Tests ◽

Term Prediction ◽

Long Term Prediction

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Risk Assessment and Decision-Making Based on Mean-CVaR-Entropy for Flood Control Operation of Large Scale Reservoirs

Water ◽

10.3390/w11040649 ◽

2019 ◽

Vol 11 (4) ◽

pp. 649 ◽

Cited By ~ 2

Author(s):

Quansen Wang ◽

Jianzhong Zhou ◽

Kangdi Huang ◽

Ling Dai ◽

Gang Zha ◽

...

Keyword(s):

Risk Assessment ◽

Decision Making ◽

Flood Control ◽

Large Scale ◽

Yangtze River Basin ◽

Mean Value ◽

Operation System ◽

Control Operation ◽

Flood Control Operation ◽

Reservoir Flood Control Operation

The risk inevitably exists in the process of flood control operation and decision-making of reservoir group, due to the hydrologic and hydraulic uncertain factors. In this study different stochastic simulation methods were applied to simulate these uncertainties in multi-reservoir flood control operation, and the risk caused by different uncertainties was evaluated from the mean value, extreme value and discrete degree of reservoir occupied storage capacity under uncertain conditions. In order to solve the conflict between risk assessment indexes and evaluate the comprehensive risk of different reservoirs in flood control operation schemes, the subjective weight and objective weight were used to construct the comprehensive risk assessment index, and the improved Mahalanobis distance TOPSIS method was used to select the optimal flood control operation scheme. The proposed method was applied to the flood control operation system in the mainstream and its tributaries of upper reaches of the Yangtze River basin, and 14 cascade reservoirs were selected as a case study. The results indicate that proposed method can evaluate the risk of multi-reservoir flood control operation from all perspectives and provide a new method for multi-criteria decision-making of reservoir flood control operation, and it breaks the limitation of the traditional risk analysis method which only evaluated by risk rate and cannot evaluate the risk of the multi-reservoir flood control operation system.

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Novel robust time series analysis for long-term and short-term prediction

Scientific Reports ◽

10.1038/s41598-021-91327-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hiroshi Okamura ◽

Yutaka Osada ◽

Shota Nishijima ◽

Shinto Eguchi

Keyword(s):

Robust Regression ◽

Simulated Data ◽

New Method ◽

Short Term ◽

Least Absolute Deviations ◽

Term Prediction ◽

Regression Approach ◽

Short Term Prediction ◽

Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

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