Table 7 Comparison of multiple regression and SCS methods across different return Periods.
From: Evaluating machine learning efficiency and accuracy for real time flash flood mapping
Method | T2 | T5 | T10 | T25 | T50 | T100 | T200 |
|---|---|---|---|---|---|---|---|
Durbin-Watson | Regression | 1.74 | 1.99 | 2.136 | 2.383 | 2.565 | 2.678 |
SCS | 1.418 | 1.335 | 1.433 | 1.691 | 1.846 | 1.898 | |
Coefficient of determination | Regression | 0.768 | 0.758 | 0.735 | 0.679 | 0.609 | 0.517 |
SCS | 0.363 | 0.25 | 0.212 | 0.194 | 0.198 | 0.179 | |
RMSE | Regression | 17.882 | 38.035 | 57.64 | 100.789 | 154.699 | 248.699 |
SCS | 63.06 | 63.261 | 116.337 | 129.912 | 190.215 | 287.446 | |
Nash-sutcliffe | Regression | 0.758 | 0.742 | 0.733 | 0.671 | 0.608 | 0.516 |
SCS | −2.005 | 0.287 | −0.087 | −0.454 | −0.408 | −0.353 |
