Faculty of Engineering/Faculty of Civil Engineering, University of Rijeka
Abstract
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
