Study on Statistical Prediction Method of Storm Surges in Seto Inland Sea

APAC 2019 ◽  
2019 ◽  
pp. 153-160
Author(s):  
K. Yokoyama ◽  
T. Yasuda
Keyword(s):  
Prediction Method ◽  
Storm Surges ◽  
Statistical Prediction ◽  
Seto Inland Sea

scholarly journals Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model

2020 ◽  
Vol 8 (12) ◽  
pp. 1028
Author(s):  
Wagner Costa ◽  
Déborah Idier ◽  
Jérémy Rohmer ◽  
Melisa Menendez ◽  
Paula Camus
Keyword(s):  
Storm Surge ◽  
Storm Surges ◽  
Absolute Error ◽  
Weather Type ◽  
Statistical Prediction ◽  
Daily Maximum ◽  
Maximum Absolute Error ◽  
Extreme Storm ◽  
Gauge Station ◽  
Data Driven Approach

Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%).

scholarly journals Spatial Economics Model Predicting Transport Volume

2016 ◽  
Vol 23 (s1) ◽  
pp. 36-43
Author(s):  
Bo Lu
Keyword(s):  
Prediction Model ◽  
Regional Economics ◽  
Prediction Method ◽  
Statistical Prediction ◽  
Spatial Economics ◽  
Industrial Economics ◽  
Classical Economics ◽  
Leading Industry ◽  
Improvement Effect ◽  
Transport Volume

Abstract It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

scholarly journals Suite of the Statistical Prediction Method for Air-Sea CO2 Flux in Shallow Water Areas

2013 ◽  
Vol 69 (2) ◽  
pp. I_1416-I_1420
Author(s):  
Kazufumi TADA ◽  
Tatsuki TOKORO ◽  
Kenta WATANABE ◽  
Tomohiro KUWAE
Keyword(s):  
Shallow Water ◽  
Prediction Method ◽  
Co2 Flux ◽  
Statistical Prediction

Classification and Prediction of Economic Losses - Storm Surge Disasters in Guangdong Province of China

2013 ◽  
Vol 709 ◽  
pp. 928-935
Author(s):  
Ling Di Zhao ◽  
Qing Hao
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Storm Surges ◽  
Guangdong Province ◽  
Entropy Method ◽  
Decision Makers ◽  
Economic Losses ◽  
Analysis Method ◽  
Direct Economic Losses ◽  
Economic Indices

This paper took Guangdong province as an example, using the statistical data of twenty times storm surges from 2003 to 2010 to evaluate the disasters and predict the economic losses. We expected it to supply with sound references and proof for the decision-makers to prevent storm surges. With economic indices of direct economic losses, collapsed houses, damaged farmland area, et al., this paper used entropy method and factor analysis method to grade the storm surges into separate levels, which are the mild disaster, the moderate disaster, the serious disaster and the extra serious disaster. By BP neural networks and gray prediction method, we established the evaluation and prediction models of direct economic losses. Comparing the results of both methods, it found that neural network is more applicable and accurate to predict the economic losses of storm surges.

NUMERICAL STUDY OF STORM SURGES IN THE SETO INLAND SEA BY MULTI PHYSICS MODEL

2009 ◽  
Author(s):  
TOMOHIRO YASUDA ◽  
TATSUYA YAMAGUCHI ◽  
SOO YOUL KIM ◽  
HIROAKI SHIMADA ◽  
TAISUKE ISHIGAKI ◽  
...  
Keyword(s):  
Numerical Study ◽  
Storm Surges ◽  
Seto Inland Sea ◽  
The Seto Inland Sea ◽  
Physics Model

Research of the VLF Atmospheric Noise Statistical Prediction

2014 ◽  
Vol 716-717 ◽  
pp. 1303-1307
Author(s):  
Guang Ming Li ◽  
Chang Jun Li ◽  
Quan Na Li
Keyword(s):  
Historical Data ◽  
Prediction Method ◽  
Data Fitting ◽  
Measured Data ◽  
Statistical Prediction ◽  
Critical Parameters ◽  
Communication Effectiveness ◽  
Atmospheric Noise ◽  
Fitting Algorithm ◽  
Geographical Position

The nature of the vlf atmospheric noise can have a dramatic effect on the performance of the vlf receivers. To understanding how the receivers will perform in different locations and time, the key is proper predicting the critical parameters of the vlf atmospheric noise. Based on the purpose, according to the historical data and the corresponding frequency and parameter variation chart, established the data fitting algorithm to predict the critical parameters of the vlf atmospheric noise. By simulating and comparing with the measured data, testified the effectiveness of the vlf atmospheric noise prediction method. To grasp the change rule of critical vlf noise parameters with time and geographical position is helpful to improve the vlf communication effectiveness.

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