# Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

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## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Study Area

#### 2.2. Data

## 3. Methods

#### 3.1. Sea Fog Identification

#### 3.1.1. Sample Selection of Sea Fog

#### 3.1.2. Feature Selection

#### 3.1.3. Sea Fog Recognition Method Based on MLP

- Step 1: Normalize the sample values, which are scaled between [0, 1];
- Step 2: Divide the training set and testing set, and train the MLP classifier using the former;
- Step 3: Cross validation to determine the optimal parameters; and
- Step 4: Complete MLP training and verify the accuracy of the classifier.

#### 3.2. Navigation Risk Assessment Model

#### 3.2.1. Criteria for Navigation Risk

#### 3.2.2. Data Processing

#### 3.2.3. Weighting the Criteria Using CRITIC

- Step 1. The accident value of each criterion is standardized using Equation (7).
- Step 2. The standardized data are organized into an evaluation matrix. The columns represent different criteria, and the rows represent the standardized value of the same criteria, as shown in Equation (8):$$\left[\begin{array}{ccc}\begin{array}{cc}{x}_{11}^{\prime}& {x}_{12}^{\prime}\\ {x}_{21}^{\prime}& {x}_{22}^{\prime}\end{array}& \cdots & \begin{array}{c}{x}_{1k}^{\prime}\\ {x}_{2k}^{\prime}\end{array}\\ \begin{array}{cc}\vdots & \vdots \end{array}& \ddots & \vdots \\ \begin{array}{cc}{x}_{n1}^{\prime}& {x}_{n2}^{\prime}\end{array}& \cdots & {x}_{nk}^{\prime}\end{array}\right],$$
- Step 3. Calculate the correlation coefficients, and a correlation coefficient matrix is obtained, expressed as Equation (9). Then, the conflict coefficient of each evaluation factor is calculated by Equation (10):$$\left[\begin{array}{ccc}\begin{array}{cc}{r}_{11}& {r}_{12}\\ {r}_{21}& {r}_{22}\end{array}& \cdots & \begin{array}{c}{r}_{1k}\\ {r}_{2k}\end{array}\\ \begin{array}{cc}\vdots & \vdots \end{array}& \ddots & \vdots \\ \begin{array}{cc}{r}_{k1}& {r}_{k2}\end{array}& \cdots & {r}_{kk}\end{array}\right],$$$${R}_{z}={\displaystyle \sum}_{m=1}^{k}\left(1-{r}_{z,m}\right)z\in \left[1,\dots ,k\right],$$
- Step 4. Calculate the sample standard deviation of each criterion using Equation (11):$${\mathrm{S}}_{z}=\sqrt{\frac{{\sum}_{l=1}^{n}{\left({x}_{l}^{\prime}-\overline{{x}^{\prime}}\right)}^{2}}{n-1}},$$
- Step 5. The amount of information for criterion $z$ is calculated with the conflict coefficient ${R}_{z}$ obtained in Step 3 via Equation (12):$${C}_{z}={S}_{z}\xb7{R}_{z},$$
- Step 6. Normalize the amount of information and get the CRITIC weights for each criterion following Equation (13):$${w}_{z}^{c}=\frac{{C}_{z}}{{\sum}_{m=1}^{k}{C}_{m}},$$

#### 3.2.4. Shipping Routes Subdivision

- Step 1: Merge and simplify the dense shipping routes;
- Step 2: Generate equally spaced route points along the simplified shipping routes;
- Step 3: Use the Delaunay triangulation method to generate a regional Tyson polygon network with route points as the source;
- Step 4: Merge polygons from the same source to form a route subdivision;
- Step 5: Trim the boundaries manually.

#### 3.3. Validation Methods

#### 3.3.1. Sea Fog Identification Validation Using the CALIPSO-VFM Points

#### 3.3.2. Navigation Risk Evaluation Validation Using the Accident Points

#### 3.3.3. Sensitivity Analysis

## 4. Results

#### 4.1. Sea Fog Identification

#### 4.1.1. Identification Precision

#### 4.1.2. Sea Fog Statistics

#### 4.2. Navigation Risk Evaluation Validation

#### 4.3. Sensitivity Analysis

#### 4.4. Navigation Risk Analysis

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Sample selection using CALIPSO-VFM data, (

**a**) AHI image on 15 June 2018 at 05:10 UTC. (

**b**) Objects classification of CALIPSO-VFM data from 15 June 2018 at 05:17 UTC.

**Figure 5.**Spectral analysis, (

**a**) the spectral reflectivity of bands 1–6 and NDSI for AHI data; (

**b**) the brightness temperature of bands 7–16 for AHI data, the red box represents the regions of bands 13, 14, and 15 with obvious differences in brightness temperature of each objects.

**Figure 8.**Channel area division process, (

**a**) the simplified shipping routes and points; (

**b**) the regional Tyson polygon using Delaunay triangulation method with route points as the source; (

**c**) shipping route areas after merging and trimming.

**Figure 9.**Distribution of CALIPSO-VFM verification points around UTC 05:00 from January to September in 2018.

**Figure 11.**Navigation risk assessment and accident points matching results in (

**a**) spring; (

**b**) summer; (

**c**) autumn; (

**d**) winter.

**Figure 13.**Risk distribution in the waterway areas in (

**a**) spring; (

**b**) summer; (

**c**) autumn; (

**d**) winter.

Name | Source | Data Description | Time | Purpose |
---|---|---|---|---|

AHI data | Japan Aerospace Exploration Agency (https://www.eorc.jaxa.jp/ptree (accessed on 1 July 2021)) | Remote sensing images from the AHI sensor [10] | 2015–2020 | Sea fog identification and calculating the frequency of monthly sea fog |

CALIPSO-VFM | National Aeronautics and Space Administration (https://search.earthdata.nasa.gov/search (accessed on 7 July 2021 )) | Classified data of clouds and aerosols from CALIPSO lidar level-2 product [27] | 2018 | Sea fog samples and verifying the accuracy |

Wind | European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/ (accessed on 15 June 2021)) | 10 m wind reanalysis data from ERA5 [28] | 2015–2020 | Calculating the frequency of strong wind |

Sea wave | European Centre for Medium-Range Weather Forecasts (https://www.ecmwf.int/ (accessed on 15 June 2021)) | Significant Wave Height reanalysis data from ERA5 [28] | 2015–2020 | Calculating the frequency of big waves |

Ocean currents | National oceanic and atmospheric administration (https://www.ncei.noaa.gov/ (accessed on 16 June 2021)) | Analyses currents data from HYCOM [29] | 2016–2020 | Calculating the frequency of big currents |

Water depth | British Oceanographic Data Centre (https://www.bodc.ac.uk/ (accessed on 16 June 2021)) | Global terrain model from GEBCO [30] | 2019 | Obtaining water depth data |

Accident data | Maritme Safety Admininstration of the people’s Republic of China (https://www.msa.gov.cn/ (accessed on 1 March 2021)) | Accident data collected and disclosed by the authorities | 2015–2020 | Weighting factors and validating the risk model |

Shipping routes | Maritme Safety Admininstration of the people’s Republic of China (https://www.msa.gov.cn/ (accessed on 1 March 2021)) | Officially recommended shipping route [31] | 2019 | Subdivision shipping route |

Fog | Wind | Wave | Currents | Water Depth | Rainfall | Total | |
---|---|---|---|---|---|---|---|

Number | 13 | 17 | 18 | 7 | 9 | 4 | 68 |

Proportion | 19.2% | 25.0% | 26.5% | 10.3% | 13.2% | 5.8% | 100% |

Ranking | 3 | 2 | 1 | 5 | 4 | 6 |

Wind | Wave | Sea Fog | Currents | Water Depth | |
---|---|---|---|---|---|

Wind | 1 | 0.76 | −0.15 | 0.23 | 0.19 |

Wave | 0.76 | 1 | −0.17 | 0.04 | 0.12 |

Sea fog | −0.15 | −0.17 | 1 | −0.01 | −0.12 |

Currents | 0.23 | 0.04 | −0.01 | 1 | −0.21 |

Depth | 0.19 | 0.12 | −0.12 | −0.21 | 1 |

Name | Wind | Wave | Sea Fog | Currents | Water Depth |
---|---|---|---|---|---|

${R}_{z}$ | 2.97 | 3.24 | 4.46 | 3.95 | 4.02 |

CRITIC weights | 0.14 | 0.17 | 0.36 | 0.17 | 0.14 |

Final weights | 0.19 | 0.23 | 0.37 | 0.09 | 0.10 |

Rank | 3 | 2 | 1 | 5 | 4 |

Sea Fog | Non-Sea Fog | ||
---|---|---|---|

H | F | ||

Sea fog | MLP | 88 | 17 |

NDSI | 96 | 75 | |

M | C | ||

Non-sea-fog | MLP | 20 | 206 |

NDSI | 12 | 148 |

Methods | POD | PMD | PFD | ETS |
---|---|---|---|---|

MLP | 81.48% | 7.62% | 18.51% | 59.22% |

NDSI threshold | 88.89% | 33.63% | 11.11% | 31.61% |

Spring | Summer | Autumn | Winter | Summary | |
---|---|---|---|---|---|

Success | 6 | 6 | 6 | 2 | 20 |

All accident numbers | 8 | 7 | 8 | 6 | 29 |

Matching rate | 75% | 85% | 75% | 33% | 69.9% |

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## Share and Cite

**MDPI and ACS Style**

Du, P.; Zeng, Z.; Zhang, J.; Liu, L.; Yang, J.; Qu, C.; Jiang, L.; Liu, S.
Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China. *Remote Sens.* **2021**, *13*, 3530.
https://doi.org/10.3390/rs13173530

**AMA Style**

Du P, Zeng Z, Zhang J, Liu L, Yang J, Qu C, Jiang L, Liu S.
Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China. *Remote Sensing*. 2021; 13(17):3530.
https://doi.org/10.3390/rs13173530

**Chicago/Turabian Style**

Du, Pei, Zhe Zeng, Jingwei Zhang, Lu Liu, Jianchang Yang, Chuanping Qu, Li Jiang, and Shanwei Liu.
2021. "Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China" *Remote Sensing* 13, no. 17: 3530.
https://doi.org/10.3390/rs13173530