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 aim and 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

Predicition of groundwater level on Grohovo landslide using ruled based regression

In order to contribute to understanding the effect of atmospheric conditions on the groundwater level fluctuations on Grohovo landslide, a machine learning tool for induction of models in form of the set of rules was applied on a dataset comprising daily atmospheric and groundwater level data measured in 2012. The atmospheric data comprises of an average daily air temperature, humidity, wind speed, pressure, total evapotranspiration, and precipitations. For the experiment independent variables i.e. atmospheric data and present groundwater level were used to model target variable i.e. predicted groundwater level for 24 and 48 hours in advance. The presented models give predictions 24 (first model) and 48 (second model) hours in advance for groundwater level fluctuations on Grohovo landslide. The first model is consisted from seven, and the second model from five rules. Both models have very high correlation coefficients of 0.99 and 0.97, respectively. From the given models, it can be concluded that the most influence on the groundwater level fluctuations have sum of daily precipitations and average daily air temperature. The obtained models are intended for use in the models for debris flow propagation on the Rječina River as a part of an Early Warning System

Relating nutrient ratios to mucilage events in Northern Adriatic

The north western part of the northern Adriaticexhibits eutrophic to mesotrophic characteristicswith recurrent algal blooms and quiteunpredictable mucilage events. To contribute to theunderstanding of the mucilage events in thenorthern Adriatic, a machine learning algorithmfor induction of regression trees was applied to adata set comprising physical and chemicalparameters, measured at six stations on the profilefrom the Po River delta (Italy) to Rovinj on thewestern Istrian coast (Croatia). A modeldescribing the connection between the TIN/PO 4ratio, considered as a necessary factor andsometimes even a trigger for mucilage events, andthe environmental conditions in northern Adriaticwas elaborated. The model for TIN/PO 4 ratioconfirmed the assumption that the mucilage eventsare connected with the changes of this ratio in thesystem. This indicates that at certain levels of Plimitation (TIN/PO 4 signal indicate) mucilageevent frequency increases. The model also revealswhich triggers are responsible (salinity andtemperature) for the changes of the TIN/PO 4 ratioas well as their threshold values. As contrasted toto the TIN/PO 4 ratio, the mucilage events could notbe attributed to the TIN/SiO 4 ratio