A novel visual modeling system for time series forecast: application to the domain of hydrology

Accurate and reliable forecasts of key hydrological variables such as stream flow are of importance due to their profound impacts on real world water resources applications. Data-driven methods have proven their applicability to modeling complex and non-linear hydrological processes. This paper presents a novel visual modeling system that has been developed to overcome the problems involved in implementation of data-driven models for hydrological forecasts using conventional programming languages: problems such as the effort and skill needed to program the models, the lack of reusability of existing models, and the lack of shared tools to perform tedious tasks such as preprocessing data. The system provides an integrated visual modeling environment within which users are able to graphically design and verify specific forecasting models for particular problems without writing code. A set of popular data-driven models are offered by the system. Plug-in models created by wrapping existing code are also allowed to run within the system due to the system's open architecture. The system's feasibility and capability is demonstrated through a case study of forecasting 1-day ahead flow in a river basin located in China. The encouraging simulation results show that the system can simplify the process of implementing hydrological forecast.