The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Meteorological Data
2.2.2. Socio-Economic Data
2.2.3. Data Processing
2.3. Methods
2.3.1. Gradient Boosting Regression Trees (GBR)
- Initialize the parameters in the learning machine with the following equation:
- 2.
- A regression tree is generated with J leaf nodes, described as follows:
- 3.
- Estimating the value of the leaf nodes in the regression tree. The value can be estimated by the following equation:
- 4.
- The learning machine of this iteration can be obtained, as shown in the following:
- 5.
- After iterations, the final regression model can be shown as follows:
2.3.2. Input-Output (IO) Model
2.3.3. The Pre-Disaster Prediction System
3. Results & Discussion
3.1. Direct Economic Loss Prediction
3.2. Indirect Economic Loss Prediction
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Criteria | Indicators | Variables |
---|---|---|
Disaster-inducing factors | Year | X1 |
Daily Maximum Precipitation | X2 | |
Precipitation Anomaly Percentage | X3 | |
Precipitation Anomaly Percentage in Spring | X4 | |
Precipitation Anomaly Percentage in Summer | X5 | |
Precipitation Anomaly Percentage in Autumn | X6 | |
Precipitation Days | X7 | |
Moderate Rainy Days | X8 | |
Heavy Rainy Days | X9 | |
Torrential Rainy Days | X10 | |
Maximum Continuous Precipitation | X11 | |
Maximum Annual Rainfall | X12 | |
Maximum Annual Continuous Rainy Days | X13 | |
Disaster-affected bodies | Casualties | X14 |
Death Toll | X15 | |
Sown Area with 10% Reduced Production | X16 | |
Sown Area with 30% reduced production | X17 | |
Sown Area with 80% reduced production | X18 | |
Railway Disruption | X19 | |
Road Disruption | X20 | |
Reservoir Loss | X21 | |
Province | X22 | |
Disaster Prevention Capabilities | Number of Reservoirs | X19 |
Capacity of Reservoirs | X20 | |
Area with Flood Prevention Measures | X21 | |
Areas with Soil Erosion under Control | X22 | |
City Sewage Pipes Length | X23 |
RMSE | MAE | EV | R2 | |
---|---|---|---|---|
Bayesian Ridge | 32.80 | 24.61 | 0.86 | 0.84 |
Linear Regression | 36.66 | 28.97 | 0.84 | 0.80 |
Elastic Net | 34.03 | 26.06 | 0.85 | 0.82 |
XGB | 31.76 | 18.64 | 0.85 | 0.85 |
GBR | 25.57 | 16.49 | 0.90 | 0.90 |
Province | Predicted Direct Economic Losses (PDEL, billion RMB) | PDEL/ Provincial GDP (‰) | Province | Predicted Direct Economic Losses (PDEL, billion RMB) | PDEL/ Provincial GDP (‰) |
---|---|---|---|---|---|
Beijing | 3.84 | 0.02 | Hubei | 2.23 | 0.15 |
Tianjin | 0.2 | 0.05 | Hunan | 0.64 | 0.05 |
Hebei | 1.28 | 0.14 | Guangdong | 32.55 | 0.11 |
Shanxi | 0.16 | 0.04 | Guangxi | 3.92 | 0.32 |
Inner Mongolia | 5.77 | 0.86 | Hainan | 0.64 | 0.23 |
Liaoning | 2.71 | 0.42 | Chongqing | 3.79 | 0.03 |
Jilin | 6.77 | 0.3 | Sichuan | 32.07 | 0.22 |
Heilongjiang | 5.08 | 1.73 | Guizhou | 4.14 | 0.08 |
Shanghai | 7.08 | 0.02 | Yunnan | 4.42 | 0.11 |
Jiangsu | 3.98 | 0.08 | Xizang | 3.68 | 0.16 |
Zhejiang | 2.08 | 0.04 | Shaanxi | 3.7 | 0.06 |
Anhui | 4.63 | 0.39 | Gansu | 10.15 | 0.75 |
Fujian | 2.32 | 0.07 | Qinghai | 2.25 | 0.17 |
Jiangxi | 3.81 | 0.42 | Ningxia | 0.88 | 0.12 |
Shandong | 20.04 | 0.94 | Xinjiang | 1.15 | 0.06 |
Henan | 3.1 | 0.35 |
Sector | Agriculture Forestry Animal Husbandry and Fishery | Food and Tobacco Processing | Manufacture of Chemical Products | Smelting and Processing of Metals | Repair of Metal Products, Machinery and Equipment | Wholesale and Retail Trades | Real Estate | |
---|---|---|---|---|---|---|---|---|
Province | ||||||||
Beijing | 0.28 | 0.28 | 0.23 | 0.13 | 0.18 | 0.11 | 0.22 | |
Tianjin | 0.24 | 0.26 | 0.20 | 0.06 | 0.06 | 0.14 | 0.07 | |
Hebei | 0.19 | 0.19 | 0.11 | 0.02 | 0.05 | 0.06 | 0.02 | |
Shanxi | 0.16 | 0.06 | 0.20 | 0.03 | 0.04 | 0.04 | 0.01 | |
Inner Mongolia | 0.22 | 0.12 | 0.13 | 0.02 | 0.04 | 0.09 | 0.03 | |
Liaoning | 0.33 | 0.18 | 0.21 | 0.04 | 0.05 | 0.05 | 0.02 | |
Jilin | 0.30 | 0.18 | 0.15 | 0.05 | 0.03 | 0.06 | 0.04 | |
Heilongjiang | 0.32 | 0.10 | 0.13 | 0.01 | 0.03 | 0.05 | 0.02 | |
Shanghai | 0.24 | 0.21 | 0.25 | 0.04 | 0.06 | 0.14 | 0.10 | |
Jiangsu | 0.20 | 0.13 | 0.19 | 0.04 | 0.04 | 0.06 | 0.03 | |
Zhejiang | 0.09 | 0.11 | 0.22 | 0.03 | 0.08 | 0.07 | 0.02 | |
Anhui | 0.23 | 0.16 | 0.18 | 0.04 | 0.04 | 0.05 | 0.04 | |
Fujian | 0.03 | 0.02 | 0.22 | 0.00 | 0.03 | 0.00 | 0.01 | |
Jiangxi | 0.04 | 0.02 | 0.35 | 0.00 | 0.02 | 0.02 | 0.02 | |
Shandong | 0.03 | 0.06 | 0.21 | 0.05 | 0.11 | 0.18 | 0.05 | |
Henan | 0.03 | 0.01 | 0.29 | 0.03 | 0.05 | 0.06 | 0.02 | |
Hubei | 0.02 | 0.01 | 0.08 | 0.07 | 0.04 | 0.05 | 0.02 | |
Hunan | 0.22 | 0.18 | 0.17 | 0.02 | 0.04 | 0.04 | 0.02 | |
Guangdong | 0.21 | 0.26 | 0.13 | 0.04 | 0.05 | 0.04 | 0.02 | |
Guangxi | 0.02 | 0.00 | 0.09 | 0.00 | 0.01 | 0.00 | 0.01 | |
Hainan | 0.10 | 0.12 | 0.20 | 0.01 | 0.02 | 0.10 | 0.05 | |
Chongqing | 0.09 | 0.09 | 0.10 | 0.02 | 0.05 | 0.04 | 0.03 | |
Sichuan | 0.22 | 0.14 | 0.20 | 0.02 | 0.02 | 0.04 | 0.02 | |
Guizhou | 0.20 | 0.04 | 0.16 | 0.02 | 0.05 | 0.08 | 0.01 | |
Yunnan | 0.21 | 0.07 | 0.15 | 0.02 | 0.04 | 0.04 | 0.03 | |
Xizang | 0.25 | 0.17 | 0.15 | 0.01 | 0.03 | 0.05 | 0.02 | |
Shaanxi | 0.17 | 0.11 | 0.21 | 0.04 | 0.03 | 0.05 | 0.03 | |
Gansu | 0.19 | 0.06 | 0.17 | 0.02 | 0.06 | 0.06 | 0.02 | |
Qinghai | 0.16 | 0.14 | 0.14 | 0.02 | 0.07 | 0.05 | 0.01 | |
Ningxia | 0.20 | 0.14 | 0.19 | 0.02 | 0.09 | 0.09 | 0.03 | |
Xinjiang | 0.27 | 0.09 | 0.29 | 0.01 | 0.03 | 0.07 | 0.01 |
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Chen, A.; You, S.; Li, J.; Liu, H. The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model. Atmosphere 2021, 12, 1448. https://doi.org/10.3390/atmos12111448
Chen A, You S, Li J, Liu H. The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model. Atmosphere. 2021; 12(11):1448. https://doi.org/10.3390/atmos12111448
Chicago/Turabian StyleChen, Anqi, Shibing You, Jiahao Li, and Huan Liu. 2021. "The Economic Loss Prediction of Flooding Based on Machine Learning and the Input-Output Model" Atmosphere 12, no. 11: 1448. https://doi.org/10.3390/atmos12111448