Numerical weather forecasts are prone to uncertainty coming from inaccuracies in the initial and boundary conditions and lack of precision in numerical models. Ensemble of forecasts partially addresses these problems by considering several runs of the numerical model. Each forecast is generated with different initial and boundary conditions and different model configurations [GR05]. The ensembl...

This paper reviews non-probabilistic approaches of modelling uncertainty, particularly in flood forecasting and introduces a fuzzy set theory-based method for treating precipitation uncertainty in rainfall-runoff modelling, which allows the temporal and/or spatial disaggregation of precipitation. The results of the fuzzy set theory-based method are compared with the probabilistic approach using...

A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedi...

Time series forecasting (i.e. the prediction of unknown future time series values using known data) has found several applications in a number of fields, like, economics and electricity forecasting [15]. Most of the used forecasting models deliver a so-called point forecast [4], a value that according to the models’ criteria is most likely to occur. Nonetheless, such forecasts lack information ...

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)'s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essent...

The purpose of this paper is present probabilistic neural networks (PNN) as an alternative quantitative technique to both linear discriminant analysis (LDA) and backpropagated neural networks (BPNN) for forecasting corporate solvency. Although traditionally this task has been approached with rather simpler linear techniques such as LDA, there is increasing empirical evidence of the superiority ...

In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR. Unlike the previous our approach utilizes native range view representation of LiDAR, which enables to operate at full sensor in real-time without voxelization or compression data. We propose a new multi-sweep fusion architecture, extracts merges temporal features directly images. F...