# Application of Multi-Source Data Fusion Method in Updating Topography and Estimating Sedimentation of the Reservoir

^{1}

^{2}

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## Abstract

**:**

^{3}, which is mainly concentrated in the areas with an elevation below 50 m and above 60 m.

## 1. Introduction

## 2. Study Area and Data Sources

#### 2.1. Study Area and Background

#### 2.2. Data Source and Distribution

## 3. Multi-Source Data Fusion Method

- Unify the format and coordinate system of topographic maps, sonar detection data, and observation points before data fusion.
- Analyze the point density, timeliness, coverage, and other index characteristics of each set of data, then sort and classify the source data according to the density and time.
- Use the difference method to correct or reduce the accuracy errors caused by different acquisition times for the data with similar sampling density [25].
- Use the ordinary kriging interpolation method based on the spherical semi-variogram model to obtain the integrated terrain of the reservoir area.

#### 3.1. Data Preprocessing

#### 3.2. Data Characteristic Analysis

- Sonar sounding data has a high accuracy of water depth and high density of mining points, but its coverage is narrow, mainly concentrated in the deep-water area from the reservoir center to the front of the dam.
- The elevation points of the topographic map data are sparsely distributed and have a lower accuracy, but almost cover the whole reservoir area.

- Data with low-density points and poor timeliness, such as the topographic mapping data;
- Data with low-density points and strong timeliness, such as the data of direct observation terrain;
- Data with high-density points and strong timeliness, such as the data of aerial sounding.

#### 3.3. Time-Effect Correction of Data

#### 3.4. Correction of Data with Density Differences

#### 3.5. Error Analysis of Interpolation Methods

## 4. Results and Analysis

#### 4.1. Comparison of Multi Method Results

#### 4.2. Verification of Precision

^{2}= 0.9909). The average simulation error of each verification point is 0.291 m, the average error percent is 0.56%, and the root mean square error is 0.752 m. It suggests that the simulation results can, overall, better reflect the actual topographic conditions, but there are a few extreme points with large errors. By combining the distribution of verification points and the prediction error (Figure 11), the points with greater simulation deviations can be seen in two types of areas.

#### 4.3. Rationality Assessment

#### 4.4. Estimation of Erosion and Sedimentation of the Reservoir

^{3}according to the difference curve. It can be seen that the volume difference fluctuates with the change of water level (Figure 15), which is caused by silting or erosion in the different areas of the reservoir. We divided the water level into 14 zones. The volume of sedimentation/erosion in each zone is shown in Figure 16. It is noted that the negative values refer to the volume of erosion and the positive values refer to the volume of siltation. Correspondingly, the spatial distribution of erosion/sedimentation in the reservoir is shown in Figure 17.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 8.**The results of the simulated terrain. (

**a**) The overall display of the result; (

**b**) Interpolation display of the low data density area; (

**c**) Interpolation display of the high data density area.

**Figure 9.**Results of different terrain simulation methods. (

**a**) Standard kriging interpolation method; (

**b**) The existing multi-source data fusion method; (

**c**) The improved multi-source data fusion method.

**Figure 10.**Comparison of the results using different methods. (

**a**) The standard kriging method; (

**b**) A section of the standard kriging method; (

**c**) The improved multi-source data fusion method; (

**d**) A section of the improved multi-source data fusion method; (

**e**) The existing multi-source data fusion method; (

**f**) A section of the existing multi-source data fusion method.

**Figure 13.**Contrast between the satellite image and the river network of the updated terrain (locations

**A**and

**B**: the examples for deviations).

No. | Measured (m) | Predicted (m) | Error (m) | Error Percent | |
---|---|---|---|---|---|

1 | 41.154 | 41.163 | 0.009 | 0.02% | |

2 | 38.138 | 38.159 | 0.021 | 0.06% | |

3 | 39.140 | 39.002 | 0.138 | 0.35% | |

4 | 32.902 | 32.874 | 0.028 | 0.08% | |

5 | 36.051 | 35.913 | 0.138 | 0.38% | |

6 | 32.253 | 32.412 | 0.159 | 0.49% | |

… | … | … | … | … | |

123 | 44.447 | 44.374 | 0.056 | 0.13% | |

124 | 41.004 | 41.301 | 0.229 | 0.56% | |

Max error | 6.160 | R^{2} | 0.9909 | Average error | 0.291 |

Min error | 0.003 | RMSE | 0.752 | Average error percent | 0.56% |

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**MDPI and ACS Style**

Liu, Y.; Xu, S.; Zhu, T.; Wang, T.
Application of Multi-Source Data Fusion Method in Updating Topography and Estimating Sedimentation of the Reservoir. *Water* **2020**, *12*, 3057.
https://doi.org/10.3390/w12113057

**AMA Style**

Liu Y, Xu S, Zhu T, Wang T.
Application of Multi-Source Data Fusion Method in Updating Topography and Estimating Sedimentation of the Reservoir. *Water*. 2020; 12(11):3057.
https://doi.org/10.3390/w12113057

**Chicago/Turabian Style**

Liu, Yu, Shiguo Xu, Tongxin Zhu, and Tianxiang Wang.
2020. "Application of Multi-Source Data Fusion Method in Updating Topography and Estimating Sedimentation of the Reservoir" *Water* 12, no. 11: 3057.
https://doi.org/10.3390/w12113057