# Support Vector Regression

Full Capacity Protocols in emergency departments usually start only after crowding thresholds are crossed, yet hallway beds, temporary nurses and transfer restrictions take time to arrange. A discrete-event simulation evaluation embedded a machine learning model forecasting waiting-room counts six hours ahead into the emergency flow and used those forecasts to trigger the protocol early. Over a simulated year, early activation significantly shortened time above the crowding threshold and average protocol duration at all three threshold settings, while changes in average emergency length of stay and wait time were not statistically significant.

Full Capacity Protocols in emergency departments usually start only after crowding thresholds are crossed, yet hallway beds, temporary nurses and transfer restrictions take time to arrange. A discrete-event simulation evaluation embedded a machine learning model forecasting waiting-room counts six hours ahead into the emergency flow and used those forecasts to trigger the protocol early. Over a simulated year, early activation significantly shortened time above the crowding threshold and average protocol duration at all three threshold settings, while changes in average emergency length of stay and wait time were not statistically significant.

Using MAWS observations, this study evaluated the bias in BMKG Ina-Flows sea surface temperature forecasts and compared three machine-learning bias-correction algorithms (SVR, LSTM, Bi-LSTM) at Karimun Jawa and Bira, finding a warm bias at both sites, with LSTM best at Karimun Jawa (RMSE 0.207, MAE 0.182, MBE -0.171, a 29.83% RMSE reduction) and Bi-LSTM best at Bira (RMSE 0.218, MAE 0.173, MBE 0.011, a 26.10% RMSE reduction), indicating that correction effectiveness is location-dependent and algorithm choice should rest on local validation.

Using MAWS observations, this study evaluated the bias in BMKG Ina-Flows sea surface temperature forecasts and compared three machine-learning bias-correction algorithms (SVR, LSTM, Bi-LSTM) at Karimun Jawa and Bira, finding a warm bias at both sites, with LSTM best at Karimun Jawa (RMSE 0.207, MAE 0.182, MBE -0.171, a 29.83% RMSE reduction) and Bi-LSTM best at Bira (RMSE 0.218, MAE 0.173, MBE 0.011, a 26.10% RMSE reduction), indicating that correction effectiveness is location-dependent and algorithm choice should rest on local validation.