Introduction
Related works
Proposed method
Problem definition
Gaussian process regression
Transfer Gaussian process regression
Multi-source transfer Gaussian process stacking
Risk level classification
Data collection experiments
Experimental setting and procedures
Surface domain | Description |
---|---|
Rough sand | Dry silica sand loosened and left in a rough surface condition |
Loose sand | Dry silica sand loosened and then slightly leveled |
Compacted sand | Dry silica sand loosened, then compacted and leveled |
Small rocks | Small volcanic rocks randomly and densely arranged on sand |
Gravel | Small pieces of river gravel |
Gravel with sand | Mixture of gravel and dry sand |
Sand over bedrock | Silica sand accumulated over flagstones with \(\sim\)2 cm depth |
Sand-covered bedrock | Flagstones covered with millimeter-layer of silica sand |
Bedrock | Textured flagstones arranged without large gaps between each |
Data processing
Slip-slope and power-slope dataset
Results and discussion
Reference GPR models
Surface domain | Slip | Power | ||
---|---|---|---|---|
RMSE | LOGLIK | RMSE | LOGLIK | |
Rough sand | 0.092 | 84.690 | 1.804 | – 185.559 |
Loose sand | 0.049 | 91.877 | 1.724 | –112.760 |
Compacted sand | 0.053 | 102.406 | 1.394 | –117.295 |
Small rocks | 0.286 | –11.315 | 3.022 | –133.898 |
Gravel | 0.145 | 18.488 | 2.075 | –113.108 |
Gravel with sand | 0.091 | 61.798 | 1.728 | –126.181 |
Sand over bedrock | 0.215 | 3.857 | 1.983 | –72.022 |
Sand-covered bedrock | 0.159 | 12.642 | 1.562 | –62.648 |
Bedrock | 0.110 | 17.169 | 2.267 | –56.038 |
Evaluation 1: Comparison of MS-TGPR and GPR
Target domain | Method | RMSE | LOGLIK | KLD |
---|---|---|---|---|
Rough sand | GPR-naive | 0.749 | –4414.641 | 366.378 |
GPR-conventional | 0.151 | –304.836 | 39.989 | |
MS-TGPR (proposed) | 0.213 | 144.511 | 23.088 | |
Gravel with sand | GPR-naive | 0.489 | –2525.338 | 488.280 |
GPR-conventional | 0.142 | 102.149 | 11.091 | |
MS-TGPR (proposed) | 0.104 | 131.126 | 15.669 | |
Sand over bedrock | GPR-naive | 0.572 | –2586.805 | 900.773 |
GPR-conventional | 0.356 | –2178.509 | 350.095 | |
MS-TGPR (proposed) | 0.314 | –87.785 | 15.430 | |
Sand-covered bedrock | GPR-naive | 0.460 | –2002.584 | 861.105 |
GPR-conventional | 0.388 | –1005.425 | 421.268 | |
MS-TGPR (proposed) | 0.227 | –111.624 | 7.758 |
Target domain | Source domain | \(\lambda _i\) | \(w_i\) |
---|---|---|---|
Rough sand | Compacted sand | 0.974 | 0.338 |
Small rocks | 0.929 | 0.324 | |
Gravel | 0.973 | 0.338 | |
Bedrock | 0.719 | 0.000 | |
Gravel with sand | Compacted sand | –0.419 | 0.000 |
Small rocks | 0.929 | 0.325 | |
Gravel | 0.973 | 0.339 | |
Bedrock | 0.962 | 0.336 | |
Sand over bedrock | Compacted sand | 0.954 | 0.250 |
Small rocks | 0.929 | 0.244 | |
Gravel | 0.973 | 0.255 | |
Bedrock | 0.962 | 0.252 | |
Sand-covered bedrock | Compacted sand | –0.476 | 0.000 |
Small rocks | 0.923 | 0.490 | |
Gravel | –0.476 | 0.000 | |
Bedrock | 0.962 | 0.510 |
Evaluation 2: Influence of source domains
Evaluation 3: Influence of target training data
Evaluation 4: Slip risk classification
Evaluation 5: Learning and prediction of power consumption
Target domain | RMSE | KLD |
---|---|---|
Rough sand | 1.974 | 14.117 |
Gravel with sand | 1.982 | 8.038 |
Sand over bedrock | 5.135 | 35.221 |
Sand-covered bedrock | 2.526 | 10.421 |
Target domain | Source domain | \(\lambda _i\) | \(w_i\) |
---|---|---|---|
Rough sand | Compacted sand | 0.999 | 1.000 |
Small rocks | –0.105 | 0.000 | |
Gravel | –0.466 | 0.000 | |
Bedrock | –0.446 | 0.000 | |
Gravel with sand | Compacted sand | 0.999 | 0.351 |
Small rocks | 0.740 | 0.000 | |
Gravel | 0.937 | 0.329 | |
Bedrock | 0.907 | 0.320 | |
Sand over bedrock | Compacted sand | 0.576 | 0.000 |
Small rocks | 0.945 | 1.000 | |
Gravel | 0.356 | 0.000 | |
Bedrock | 0.719 | 0.000 | |
Sand-covered bedrock | Compacted sand | 0.790 | 0.000 |
Small rocks | 0.945 | 0.509 | |
Gravel | 0.628 | 0.000 | |
Bedrock | 0.907 | 0.491 |