scholarly journals Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhijin Wang ◽  
Yaohui Huang ◽  
Bingyan He ◽  
Ting Luo ◽  
Yongming Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
High Morbidity ◽  
Infectious Diarrhea ◽  
Short Term ◽  
Search Data ◽  
Parsimonious Model ◽  
Using Data ◽  
Short Term Prediction ◽  
And Control

Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.

Short-Term Metro Ridership Prediction During Unplanned Events

2021 ◽  
pp. 036119812110375
Author(s):  
Yangyang Zhao ◽  
Zhenliang Ma ◽  
Xinguo Jiang ◽  
Haris N. Koutsopoulos
Keyword(s):  
Prediction Accuracy ◽  
Large Scale ◽  
Prediction Models ◽  
Imbalanced Data ◽  
Short Term ◽  
Training Problem ◽  
Term Prediction ◽  
Model Training ◽  
Short Term Prediction ◽  
The Impact

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

scholarly journals Neural Networks for Prediction and Control of Chaotic Fluidized Bed Hydrodynamics

Fractals ◽  
1997 ◽  
Vol 05 (03) ◽  
pp. 523-530 ◽  
Author(s):  
R. Bakker ◽  
R. J. de Korte ◽  
J. C. Schouten ◽  
C. M. Van Den Bleek ◽  
F. Takens
Keyword(s):  
Fluidized Bed ◽  
Prediction Error ◽  
Control Of Chaos ◽  
Short Term ◽  
Prediction And Control ◽  
Short Term Prediction ◽  
Capacitance Tomography ◽  
And Control ◽  
Dynamic Invariants ◽  
Tomography Data

A neural-network-based model that has learnt the chaotic hydrodynamics of a fluidized bed reactor is presented. The network is trained on measured electrical capacitance tomography data. A training algorithm is used that does not only minimize the short-term prediction error but also the information needed to synchronize the model with the real system. This forces the model to focus more on learning the longer term dynamics of the system, expressed in the average multi-step-ahead prediction error and dynamic invariants such as correlation entropy and dimension. The availability of the model is an important step towards control of chaos in gas-solid fluidized beds.

scholarly journals Using data from large scale farming operations to understand the importance of feeding the ewe to improve whole farm performance

2013 ◽  
pp. 97-102 ◽  
Author(s):  
D.R. Stevens ◽  
G. Young
Keyword(s):  
Large Scale ◽  
East Coast ◽  
Body Condition Score ◽  
Short Term ◽  
Management Decisions ◽  
Use Of Data ◽  
Condition Score ◽  
Farm Performance ◽  
Using Data

The collection and use of data from large scale farming operations provided significant insights into drivers of sheep performance. These drivers included minimum two-tooth liveweight at tupping, ewe condition and pasture cover at lambing and the importance of weaning weight on whole farm performance. Using this data to demonstrate the influence of management decisions resulted in an increase in average lamb liveweight gain between birth and weaning of approximately 20 g/day in Landcorp Farming Ltd East Coast flocks over the 4 years of monitoring. Lambing percentage was harder to change, though individual farms increased lambing percentage by up to 35% by concentrating on increasing feed allocation and maintaining ewe body condition score during winter. Low liveweight in some two-tooth ewes was inversely related to the percentage of dries in a flock and prompted more emphasis on growing replacement stock. The programme shifted focus from short-term tactical feeding and management decisions to long-term strategies such as stock and sales policies that placed the breeding flock as the major priority. Keywords: breeding ewes, data, lambing percentage, lambs, liveweight gain, whole flock analysis.

scholarly journals Short-Term Prediction of Energy Consumption in Demand Response for Blocks of Buildings: DR-BoB Approach

Buildings ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 221
Author(s):  
Nashwan Dawood
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Economic Benefits ◽  
Least Square ◽  
Percentage Error ◽  
Pilot Site ◽  
Short Term ◽  
Rls Algorithm ◽  
Term Prediction ◽  
Short Term Prediction

Load forecasting plays a major role in determining the prices of the energy supplied to end customers. An accurate prediction is vital for the energy companies, especially when it comes to the baseline calculations that are used to predict the energy load. In this paper, an accurate short-term prediction using the Exponentially Weighted Extended Recursive Least Square (EWE-RLS) algorithm based upon a standard Kalman filter is implemented to predict the energy load for blocks of buildings in a large-scale for four different European pilot sites. A new software tool, namely Local Energy Manager (LEM), is developed to implement the RLS algorithm and predict the forecast for energy demand a day ahead with a regular meter frequency of a quarter of an hour. The EWE-RLS algorithm is used to develop the LEM in demand response for blocks of buildings (DR-BOB), this is part of a large-scale H2020 EU project with the aim to generate the energy baselines during and after running demand response (DR) events. This is achieved in order to evaluate and measure the energy reduction as compared with historical data to demonstrate the environmental and economic benefits of DR. The energy baselines are generated based on different market scenarios, different temperature, and energy meter files with three different levels of asset, building, and a whole pilot site level. The prediction results obtained from the Mean Absolute Percentage Error (MAPE) offer a 5.1% high degree of accuracy and stability at a UK pilot site level compared to the asset and whole building scenarios, where it shows a very acceptable prediction accuracy of 10.7% and 19.6% respectively.

Application of Intelligence Control in Agriculture Greenhouses

2015 ◽  
Vol 719-720 ◽  
pp. 293-297 ◽  
Author(s):  
Hui Guo Lu ◽  
Cong Ying Li ◽  
Juan Ping Jiang
Keyword(s):  
Data Acquisition ◽  
Large Scale ◽  
Low Cost ◽  
Information Access ◽  
Intelligent Monitoring ◽  
Communication Technique ◽  
Using Data ◽  
National Industry ◽  
And Control ◽  
Greenhouse Control

Agriculture is the foundation of the national economy, and the guarantee of national industry. Greenhouse can grow counter-season production of crops and bring higher profits to farmers. But in our country the informatization level of agricultural is low. By using data acquisition technology, wireless communication technique and computer technology, this project can make intelligent monitoring, management and control of the large-scale plastic greenhouse come true and make agricultural information access and remote data transmission and exchange automated. The project is of simple circuit, low cost, good maintainability, and the expected results (the management of the greenhouse control) is operating conveniently and hommization, which can reduce a lot of manual work. It is expected to be widely popularized in agricultural plastic greenhouses in China. Key words:Intelligent Agriculture; Automation; Data acquisition; Sensors;

Modeling and Analyzing of Regional Fluctuation of Cocoon Silk Based on BP Neural Network

2011 ◽  
Vol 175-176 ◽  
pp. 418-423
Author(s):  
Xi Yang ◽  
Bing Di Liu ◽  
Hai Liu ◽  
Lun Bai
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Short Term ◽  
Test Analysis ◽  
Bp Network ◽  
Silk Cocoon ◽  
Bp Artificial Neural Network ◽  
Set Up ◽  
Short Term Prediction

According to the collected data of the market monthly closing price on dry cocoon and raw silk, predictive modeling and analyzing on the price trend of dry cocoon and raw silk are made based on the related theories and test analysis of BP Artificial Neural Network is carried out. Developed corresponding procedure with Neural Network toolbox under the condition of MATLAB, and then set up BP network relevant prediction models, at last, checked up with examples. At the same time, this study set up corresponding time series forecasting models and made empirical analysis on the basis of Eviews software. The results show that, the two methods both fit for short-term prediction, BP network can achieve human coordination control, the better predictive precision, which supplies an analysis way for silk cocoon market, all of that can be referred to in the future.

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