Kinetic Energy Estimation of IMU-Equipped Sediment Particles with Gaussian Process Regression and Conformal Prediction

Direct particle-scale sediment measurements remain difficult in turbid, high-energy rivers where optical methods fail. We present an integration-free inertial measurement unit (IMU) workflow that maps short windows to projected speed and kinetic energy using physics-Aware preprocessing, orientation-invariant Hankel embeddings, Gaussian process regression (GPR), and split conformal prediction. On event-disjoint hold-out tests, the selected GPR model (m = 10$ ) achieves R{2} = 0.628$ , RMSE = 0.168ms-1, and MAE = 0.096ms-1. A four-model benchmark on identical event-grouped folds (GPR, LSTM, SVR-RBF, and LSBoost) gives the lowest RMSE for 0.158ms-1; GPR is within 0.001ms-1 of the strongest non-GPR comparator (LSBoost), and paired RMSE differences are nonsignificant (p = 0.812$ ). Empirical conformal coverage is 87.6%/93.7%/97.9% for nominal 90%/95%/99% targets. River Calder deployments show peak kinetic energies up to 0.168 J. The framework provides uncertainty-Aware kinematics and energetics for autonomous sediment-Transport monitoring.