scholarly journals Application of the decomposition prediction reconstruction framework to middle and long-term runoff forecasting

2020 ◽  
Author(s):  
Yi Ji ◽  
Hong-Tao Dong ◽  
Zhen-Xiang Xing ◽  
Ming-xin Sun ◽  
Qiang Fu ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Annual Runoff ◽  
Dry Season ◽  
Validation Dataset ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Flood Season ◽  
Runoff Forecasting ◽  
Forecasting Performance

Abstract Middle and long-term runoff forecasting has always been a problem, especially in flood seasons. The forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire time series is decomposed into several sub-series, each sub-series is divided into training and validation dataset, and forecasted by some common models, such as least-squares support vector machine (LSSVM), and finally an ensemble forecasting result is obtained by summing the forecasted results of each sub-series. This model is applied to forecast the inflow runoff of theShitouxia Reservoir (STX Reservoir). The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. The root mean square error value is reduced from 20.654 to 10.235, a decrease of 50.4%.The runoff forecasting performance can be improved effectively by applying the CEEMD-LSSVM model.When analyzing the annual runoff forecasting results month by month, it was found that the forecasting results from November to April of the following year were unsatisfactory compared with the nearest neighbor bootstrapping regressive (NNBR) model which more suitable in dry season, but the forecasting results from May to October improved significantly. This also proves that the CEEMD-LSSVM model has a great advantage in the forecasting of inflow runoff during the flood season. In the optimized operation of reservoirs, the forecasting result of inflow runoff in flood season is more important than in dry season. Therefore, when forecasting annual runoff month by month, it is recommended to adopt the CEEMD-LSSVM model in the flood season and the NNBR model in the dry season, that is, the combination of the two models is applied to the forecasting of the inflow runoff of the STX Reservoir.

Determinants of rental strategy: short-term vs long-term rental strategy

2020 ◽  
Vol 13 (12) ◽  
pp. 3873-3894
Author(s):  
Sina Shokoohyar ◽  
Ahmad Sobhani ◽  
Anae Sobhani
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Sharing Economy ◽  
Real Estate Market ◽  
Support Vector ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Content Type

Purpose Short-term rental option enabled via accommodation sharing platforms is an attractive alternative to conventional long-term rental. The purpose of this study is to compare rental strategies (short-term vs long-term) and explore the main determinants for strategy selection. Design/methodology/approach Using logistic regression, this study predicts the rental strategy with the highest rate of return for a given property in the City of Philadelphia. The modeling result is then compared with the applied machine learning methods, including random forest, k-nearest neighbor, support vector machine, naïve Bayes and neural networks. The best model is finally selected based on different performance metrics that determine the prediction strength of underlying models. Findings By analyzing 2,163 properties, the results show that properties with more bedrooms, closer to the historic attractions, in neighborhoods with lower minority rates and higher nightlife vibe are more likely to have a higher return if they are rented out through short-term rental contract. Additionally, the property location is found out to have a significant impact on the selection of the rental strategy, which emphasizes the widely known term of “location, location, location” in the real estate market. Originality/value The findings of this study contribute to the literature by determining the neighborhood and property characteristics that make a property more suitable for the short-term rental vs the long-term one. This contribution is extremely important as it facilitates differentiating the short-term rentals from the long-term rentals and would help better understanding the supply-side in the sharing economy-based accommodation market.

scholarly journals An EMD-Based Chaotic Least Squares Support Vector Machine Hybrid Model for Annual Runoff Forecasting

Water ◽  
2017 ◽  
Vol 9 (3) ◽  
pp. 153 ◽  
Author(s):  
Xuehua Zhao ◽  
Xu Chen ◽  
Yongxin Xu ◽  
Dongjie Xi ◽  
Yongbo Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Hybrid Model ◽  
Annual Runoff ◽  
Support Vector ◽  
Runoff Forecasting

scholarly journals Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting

2021 ◽  
Vol 11 (12) ◽  
pp. 5658
Author(s):  
Pedro Escudero ◽  
Willian Alcocer ◽  
Jenny Paredes
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Short Term Memory ◽  
Moving Average ◽  
Validation Dataset ◽  
Short Term ◽  
Random Behavior ◽  
Forecasting Performance ◽  
Using Data

Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term.

scholarly journals Mid and long-term optimize scheduling of cascade hydro-power stations based on modified GA-POA method

2018 ◽  
Vol 379 ◽  
pp. 199-203
Author(s):  
Jiqing Li ◽  
Xiong Yang
Keyword(s):  
Large River ◽  
Annual Runoff ◽  
Optimal Model ◽  
Hydro Power ◽  
Joint Development ◽  
Flood Season ◽  
Power Stations ◽  
Generating Capacity ◽  
The Difference

Abstract. In this paper, to explore the efficiency and rationality of the cascade combined generation, a cascade combined optimal model with the maximum generating capacity is established, and solving the model by the modified GA-POA method. It provides a useful reference for the joint development of cascade hydro-power stations in large river basins. The typical annual runoff data are selected to calculate the difference between the calculated results under different representative years. The results show that the cascade operation of cascaded hydro-power stations can significantly increase the overall power generation of cascade and ease the flood risk caused by concentration of flood season.

scholarly journals Data-driven runoff forecasting for Minjiang River: a case study

2020 ◽  
Vol 20 (6) ◽  
pp. 2284-2295
Author(s):  
Yuqiang Wu ◽  
Qinhui Wang ◽  
Ge Li ◽  
Jidong Li
Keyword(s):  
Optimal Allocation ◽  
Data Driven ◽  
Function Parameter ◽  
Support Vector ◽  
Runoff Forecasting ◽  
Minjiang River ◽  
Svm Model ◽  
Runoff Prediction

Abstract Long-term runoff forecasting has the characteristics of a long forecast period, which can be widely applied in environmental protection, hydropower operation, flood prevention and waterlogging management, water transport management, and optimal allocation of water resources. Many models and methods are currently used for runoff prediction, and data-driven models for runoff prediction are now mainstream methods, but their prediction accuracy cannot meet the needs of production departments. To this end, the present research starts with this method and, based on a support vector machine (SVM), it introduces ant colony optimization (ACO) to optimize its penalty coefficient C, Kernel function parameter g, and insensitivity coefficient p, to construct a data-driven ACO-SVM model. The validity of the method is confirmed by taking the Minjiang River Basin as an example. The results show that the runoff predicted by use of ACO-SVM is more accurate than that of the default parameter SVM and the Bayesian method.

scholarly journals Mid-long term runoff forecasting model based on RS-RVM

2018 ◽  
Vol 246 ◽  
pp. 02039 ◽  
Author(s):  
Wen Zhang ◽  
Jian Hu ◽  
Yintang Wang ◽  
Leizhi Wang ◽  
Lingjie Li ◽  
...  
Keyword(s):  
Rough Set Theory ◽  
Southern Oscillation ◽  
Relevance Vector Machine ◽  
Support Vector ◽  
Forecasting Model ◽  
Physical Factors ◽  
Machine Method ◽  
Model Based ◽  
Runoff Forecasting

In view of the two key problems in hydrological mid-long term runoff forecasting-the selection of key forecasting factors and the construction of forecasting models, an analysis is made on, taking Danjiangkou Reservoir as an example, the basis of preliminarily identifying the sea-air physical factors such as atmospheric circulation, sea surface temperature and Southern Oscillation, et al. The rough set theory is used to establish the data decision table and reduce the factors, and the relevance vector machine method is adopted to establish the mid-long term runoff forecasting model based on reduced factor set. Meanwhile, this paper simulates and predicts the amount of runoff of the reservoir in September and October during the autumn floods from 1952 to 2008, and makes comparison with the model adopting support vector machine. The result shows that the relevance vector machine has better robustness and generalization performance. According to the standard of 20% annual variation, the simulation accuracy of September and October reaches 93.9% and 95.9%, respectively, and the accuracy of the trial forecasting is all up to standard. Moreover, this model better reflects the characteristics of ample flow period and low water period of the forecasting years.

scholarly journals Runoff forecasting benefit evaluation for long-term power generation scheduling

2018 ◽  
Vol 246 ◽  
pp. 01058
Author(s):  
Xiaoling Ding ◽  
Jianzhong Zhou ◽  
Xiaocong Mo ◽  
Chao Wang ◽  
Yongqiang Wang
Keyword(s):  
Power Generation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Forecast Accuracy ◽  
Annual Runoff ◽  
Accuracy Evaluation ◽  
Distribution Method ◽  
Benefit Evaluation ◽  
Runoff Forecasting

Long-term runoff forecasting important reference significance for the long-term planning of cascade hydropower stations. The traditional forecast accuracy evaluation is based on the deviation between the predicted runoff and the measured hydrological sequence, but fails to consider the effect on long-term scheduling. In this paper, a runoff forecasting evaluation method for long-term scheduling is presented. First, a monthly distribution method based on the forecast value of annual runoff is proposed to describe the uncertainty of the forecast. Then, a power generation plan model with the maximum generation objective and an actual generation benefit evaluation model are established to study the effect of runoff forecasting in scheduling. At last two indexes of “Incremental generation” and “Incremental benefit” based on the comparison of actual benefit with and without a forecast plan are given to evaluate the performance of forecasting. The case study shows that the proposed evaluation method can reflect the actual benefit brought by the forecast information, which provide more practical guidance for the hydropower station.

scholarly journals Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

2019 ◽  
Vol 9 (12) ◽  
pp. 2544 ◽  
Author(s):  
Hua ◽  
Chen ◽  
Zhang ◽  
Liu ◽  
Wen
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Forward Selection ◽  
Generalization Performance ◽  
Severity Levels ◽  
Feature Dimension

Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.

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