Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
Abstract
:1. Introduction
2. Materials and Methods
2.1. Rockfall Data Collection
2.2. Independent Variables for Rockfall Analysis
2.3. Machine Learning and Accuracy Indices
3. Results
3.1. Random Forest Model Performance in Rockfall Prediction
3.2. Feature Importance Visualization
4. Discussion
4.1. The Usefulness of Internet-Based Data Collection for Rockfall Hazards
4.2. Machine Learning Applications in Rockfall Hazard Prediction
4.3. Insights from SHAP Value Analysis
4.4. Practical Implications for Model Accuracy and Reliability
4.5. Limitations of the Study
4.5.1. Bias in Data Collection via Google Alerts
4.5.2. Omission of Severity of Rockfalls
4.5.3. Effects of Seasonal Vegetation and Precipitation Patterns
4.5.4. Effect of Climate Change on Model Prediction
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Highway Type | Num. | Total Length (km) | Num. of Rockfall | Rockfall Roads | % Rockfall Roads | Rockfall/km |
---|---|---|---|---|---|---|
National Expressway | 10 | 1061.8 | 0 | 0 | 0.0% | 0.0000 |
Provincial Highway | 97 | 5323.2 | 126 | 24 | 24.7% | 0.0237 |
City/County Highway | 158 | 3684.1 | 19 | 13 | 8.2% | 0.0052 |
District/Rural Highway | 2250 | 11362.7 | 37 | 16 | 0.7% | 0.0033 |
Exclusive Highway | 35 | 407.8 | 0 | 0 | 0.0% | 0.0000 |
No. | Variable | Description |
---|---|---|
Geographical Variables | ||
1 | twd97_y | The northing coordinate in the TWD97 coordinate system (also known as the 1997 Taiwan Datum), which is used for mapping and surveying in Taiwan. |
2 | twd97_x | The easting coordinate in the TWD97 coordinate system. |
3 | dist_river | Distance to the nearest river (meters). |
4 | dist_fault | Distance to the nearest fault line (meters). |
5 | dist_urban | Distance to the urban boundary (meters). |
6 | dist_geo_sens_area | Distance to the geologically sensitive area boundary (meters). |
7 | dist_debris_flow_stream | Distance to the potential debris flow stream (meters). |
8 | dist_debris_flow_watershed | Distance to the potential debris flow stream watershed boundary (meters). |
9 | dist_debris_flow_influence | Distance to the potential debris flow stream influence area boundary (meters). |
Topographical Variables | ||
10 | elev | Elevation from 20 m resolution DEM (meters). |
11 | slope_deg | Slope measured in degrees. |
12 | slope_asp | Slope aspect. |
13 | curvature | Slope curvature. |
14 | twi | Topographic Wetness Index. |
15 | spi | Stream Power Index. |
16 | tri | Terrain Ruggedness Index. |
17 | river_density | River density (km/km2). |
Environmental Variables | ||
18 | avg_yr_rainfall | Average yearly rainfall from 2020 to 2023 (mm/year). |
19 | avg_yr_rainy_days | Average number of rainy days per year from 2020 to 2023 (days). |
20 | avg_month_temp | Average monthly temperature from 2020 to 2023 (℃). |
21 | avg_min_temp | Average minimum temperature per year from 2020 to 2023 (℃). |
22 | avg_max_temp | Average maximum temperature per year from 2020 to 2023 (℃). |
23 | avg_month_wind_speed | Average monthly wind speed from 2020 to 2023 (m/s). |
24 | avg_month_humidity | Average monthly humidity from 2020 to 2023 (%). |
25 | ndvi | Normalized Difference Vegetation Index (NDVI). |
Geological Variables | ||
26 | epoch | Geological time period. |
27 | stratum_abbr | Stratum abbreviation. |
Socioeconomic Variables | ||
28 | pop_density | Population density (persons/ha). |
5-Fold Cross-Validation | ||||||||
---|---|---|---|---|---|---|---|---|
mtry | ntree | Accuracy | Kappa | Sensitivity | Specificity | Precision | F1 | |
1 | 1 | 100 | 0.8069 | 0.6138 | 0.8759 | 0.7379 | 0.7721 | 0.8170 |
2 | 1 | 500 | 0.7897 | 0.5793 | 0.8552 | 0.7241 | 0.7592 | 0.8008 |
3 | 1 | 1000 | 0.8000 | 0.6000 | 0.8690 | 0.7310 | 0.7654 | 0.8108 |
4 | 3 | 100 | 0.7931 | 0.5862 | 0.8552 | 0.7310 | 0.7669 | 0.8041 |
5 | 3 | 500 | 0.7862 | 0.5724 | 0.8276 | 0.7448 | 0.7688 | 0.7929 |
6 | 3 | 1000 | 0.7793 | 0.5586 | 0.8276 | 0.7310 | 0.7589 | 0.7879 |
7 | 5 | 100 | 0.7862 | 0.5724 | 0.8345 | 0.7379 | 0.7642 | 0.7946 |
8 | 5 | 500 | 0.7828 | 0.5655 | 0.8345 | 0.7310 | 0.7623 | 0.7924 |
9 | 5 | 1000 | 0.7862 | 0.5724 | 0.8276 | 0.7448 | 0.7688 | 0.7928 |
10 | 7 | 100 | 0.7897 | 0.5793 | 0.8414 | 0.7379 | 0.7658 | 0.7998 |
11 | 7 | 500 | 0.7828 | 0.5655 | 0.8276 | 0.7379 | 0.7670 | 0.7914 |
12 | 7 | 1000 | 0.7793 | 0.5586 | 0.8345 | 0.7241 | 0.7568 | 0.7904 |
13 | 9 | 100 | 0.7862 | 0.5724 | 0.8414 | 0.7310 | 0.7603 | 0.7967 |
14 | 9 | 500 | 0.7793 | 0.5586 | 0.8207 | 0.7379 | 0.7620 | 0.7865 |
15 | 9 | 1000 | 0.7931 | 0.5862 | 0.8483 | 0.7379 | 0.7702 | 0.8040 |
16 | 11 | 100 | 0.7690 | 0.5379 | 0.8276 | 0.7103 | 0.7473 | 0.7808 |
17 | 11 | 500 | 0.7931 | 0.5862 | 0.8483 | 0.7379 | 0.7679 | 0.8035 |
18 | 11 | 1000 | 0.7793 | 0.5586 | 0.8414 | 0.7172 | 0.7529 | 0.7914 |
Test Dataset | ||||||||
Accuracy | Kappa | Sensitivity | Specificity | Precision | F1 | |||
0.8514 | 0.7027 | 0.8378 | 0.8649 | 0.8611 | 0.8493 |
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Nguyen, K.A.; Jiang, Y.-J.; Huang, C.-S.; Kuo, M.-H.; Chen, W. Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways. Future Internet 2024, 16, 299. https://doi.org/10.3390/fi16080299
Nguyen KA, Jiang Y-J, Huang C-S, Kuo M-H, Chen W. Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways. Future Internet. 2024; 16(8):299. https://doi.org/10.3390/fi16080299
Chicago/Turabian StyleNguyen, Kieu Anh, Yi-Jia Jiang, Chiao-Shin Huang, Meng-Hsun Kuo, and Walter Chen. 2024. "Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways" Future Internet 16, no. 8: 299. https://doi.org/10.3390/fi16080299