Large scale, three-dimensional, laboratory experiments are performed to study tsunami generation by rigid underwater landslides. The main purpose of these experiments is to both gain insight into landslide tsunami generation processes and provide data for subsequent validation of a three-dimensional numerical model. In each experiment a smooth and streamlined rigid body slides down a plane slope, starting from different initial submergence depths, and generates surface waves. Different conditions of wave nonlinearity and dispersion are generated by varying the model slide initial submergence depth. Surface elevations are measured with capacitance gauges. Runup is measured at the tank axis using a video camera. Landslide acceleration is measured with a microaccelerometer embedded within the model slide, and its time of passage is further recorded at three locations down the slope. The repeatability of experiments is very good. Landslide kinematics is inferred from these measurements and an analytic law of motion is derived, based on which the slide added mass and drag coefficients are computed. Characteristic distance and time of slide motion, as well as a characteristic tsunami wavelength, are parameters derived from these analyses. Measured wave elevations yield characteristic tsunami amplitudes, which are found to be well predicted by empirical equations derived in earlier work, based on two-dimensional numerical computations. The strongly dispersive nature and directionality of tsunamis generated by underwater landslides is confirmed by wave measurements at gauges. Measured coastal runup is analyzed and found to correlate well with initial slide submergence depth or characteristic tsunami amplitude.

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Nowadays, numerical model data is one of the primary inputs to all metocean studies, whether for deep-water locations or coastal applications. This paper presents the use of machine learning to calibrate long term metocean time series of wind and wave parameters obtained from numerical models against measurement records, usually covering shorter periods. We present the added value of machine learning compared to standard calibration methods to improve data used as primary input to both operability studies and engineering design studies. Time series of wind and wave parameters obtained from global numerical hindcast data sets are compared to oceanographic buoy measurements. We investigate the improvement brought by machine learning methods on the quality of the calibrated populations for the bulk of the distributions, but also the agreement between the calibrated data and the measurements for extreme events, not only for peak values but also for storm profiles. We evaluate the reliability of the method by comparing the results over different periods at 1 location and with varying length of training, validation and test sets.