基于模糊贴近度的震后恢复预测模型——以日本地震经济恢复为例<br>A Forecast Model Based on Fuzzy Approach for the Recovery in Economy after the Earthquake——Illustrated by the Case in Predicting the Recovery in Japanese Economy after the Earthquake

Finance ◽  
2012 ◽  
Vol 02 (01) ◽  
pp. 1-8
Author(s):  
黄 珂
Keyword(s):  
Forecast Model ◽  
Japanese Economy ◽  
Fuzzy Approach ◽  
Model Based

A Statistical Extended-Range Tropical Forecast Model Based on the Slow Evolution of the Madden–Julian Oscillation

1999 ◽  
Vol 12 (7) ◽  
pp. 1918-1939 ◽  
Author(s):  
Duane E. Waliser ◽  
Charles Jones ◽  
Jae-Kyung E. Schemm ◽  
Nicholas E. Graham
Keyword(s):  
Forecast Model ◽  
Madden Julian Oscillation ◽  
Model Based ◽  
Extended Range ◽  
Slow Evolution

The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2366-2369 ◽  
Author(s):  
Yao Wang ◽  
Dan Zheng ◽  
Shi Min Luo ◽  
Dong Ming Zhan ◽  
Peng Nie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Good Effect ◽  
Passenger Flow ◽  
Model Based ◽  
Flow Prediction ◽  
Transport Organization ◽  
Railway Passenger ◽  
Short Time

Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

scholarly journals Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

2012 ◽  
Vol 12 (12) ◽  
pp. 3719-3732 ◽  
Author(s):  
L. Mediero ◽  
L. Garrote ◽  
A. Chavez-Jimenez
Keyword(s):  
Data Assimilation ◽  
Genetic Programming ◽  
Real Time ◽  
High Performance ◽  
Forecast Model ◽  
Flood Forecasting ◽  
Distribution Functions ◽  
Model Parameters ◽  
Hydrological Process ◽  
Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

scholarly journals Electricity price forecast model based on machine learning

2021 ◽  
Vol 7 (2) ◽  
pp. 13-26
Author(s):  
Petrică RĂDAN ◽  
◽  
Florin-Emilian CIAUȘIU ◽  
Bogdan-Ștefan ACHIM ◽  
Andrei STAN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Forecast Model ◽  
Electricity Price ◽  
Price Forecast ◽  
Model Based
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