engleski

Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water.Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aimand objective of this paper are to investigate the possibility of implementing an EWS in a small- scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Hydrological model; Small chatcment; artificial neural network; Early warning system

Indexing: Expanded Academic ASAP ; Expanded Academic Index ; GeoRef ; Google Scholar ; GREENR ; InfoTrac Custom journals ; J-Gate Portal ; Journal Citation Reports-Science Edition ; Meteorological and Geoastrophysical Abstracts ; Oceanic Abstracts ; Online Access to Research in the Environment (OARE) ; ProQuest Advanced Technologies and Aerospace Collection ; ProQuest Atmospheric Science Journals ; ProQuest Natural Science Collection ; ProQuest SciTech Collection ; ProQuest Technology Collection ; SafetyLit ; Scopus ; TEMA Database