Occasionally, consequences caused by water induced events can somewhat be reduced or even prevented with the help of an early warning system whose aim is timely notification of local population on potentially upcoming hazardous event. The motivation for this thesis arises from the need to explore the possibilities to foresee such water caused events on small catchments with an aim to mitigate its consequences by implementing an early warning system. Research and analysis shown in this theses encompasses the establishment of continuous meteorological and hydrological data monitoring, on research area Slani Potok (Vinodol Valley) historically known as potentially hazardous area, the application of the artificial neural network as a means for the development of hydrological rainfall-runoff model, defining the methods for model validation and evaluation, as well as the development of the methodology for the hydrological rainfall-runoff model implementation on small catchments. Upon on this research a hydrological rainfall-runoff model for small catchments was developed based on artificial neural network. Gathered data was used for training, validation and evaluation of model`s accuracy and precision in rainfall-runoff prediction. The model was validated and evaluated using visual and numerical quality measures according to which needed accuracy in model prediction was determined for the implementation of early warning system. Based on this model a detailed methodology for the implementation of rainfall-runoff model on small catchments developed on artificial neural network was established.