On Time-Series InSAR by SA-SVR Algorithm: Prediction and Analysis of Mining Subsidence
Table 1
Prediction results and correlation coefficients (unit: mm).
Point
20191227 SBAS predict deviation
20200108 SBAS predict deviation
20200201 SBAS predict deviation
20200213 SBAS predict deviation
20200225 SBAS predict deviation
RMSE
MAE
A17
-623.7
-632.6
-634.4
-637.3
-640.1
-623.3
-627.1
-634.9
-638.8
642.7
2.8
2.1
0.75
-0.5
-5.5
0.5
1.5
2.5
A25
-768.1
-772.0
-772.7
-781.8
-783.1
-763.0
-767.8
-777.5
-782.3
-787.2
4.1
3.8
0.51
-5.2
-4.2
4.8
0.6
4.1
B30
-740.6
-744.7
-748.0
-756.0
-759.2
-735.0
-740.6
-751.7
-757.3
-762.8
3.9
3.7
0.68
-5.6
-4.2
3.7
1.3
3.6
B45
-521.2
-523.9
-526.2
-528.0
-528.1
-522.8
-523.8
-525.8
-526.8
-527.8
0.9
0.7
0.88
1.6
-0.1
-0.4
-1.2
-0.3
Note: RMSE (root mean square error) denotes the square root of the ratio of the sum of the squares of the deviations of the observations from their true values to the number of observations; MAE (mean absolute error) denotes the average value of the absolute error and is used to measure the deviation between the observed and true values.
