Fuzzy linear regression analysis for groundwater response to meteorological drought in the aquifer system of Xanthi plain, NE Greece

Abstract

This paper studies, through the principles of fuzzy set theory, groundwater response to meteorological drought in the case of an aquifer system located in the plains at the southeast of Xanthi, NE Greece. Meteorological drought is expressed through standardized Reconnaissance Drought Index (RDISt) and Standardized Precipitation Index (SPI), which are calculated for various reference periods. These drought indices are considered as independent variables in multiple fuzzy linear regression based on Tanaka's model, while the observed water table regarding two areas is used as a dependent variable. The fuzzy linear regression of Tanaka is characterized by the inclusion constraints where all the observed data must be included in the produced fuzzy band. Hence, each fuzzy output can get an interval of values where a membership degree corresponds to each of them. A modification of the Tanaka model by adding constraints is proposed in order to avoid irrational behavior. The results show that there was a significant influence of the meteorological drought of the previous hydrological year, while geology plays an important role. Furthermore, the use of RDISt improves the results of fuzzy linear regressions in all cases. Two suitability measures and a measure of comparison between fuzzy numbers are used.

Modeling and indexing the drought severity by augmenting SPI metric system to incorporate multi-modal ground temperature data

“…According to Table 1, although the favorable conditions of rainfall and temperature agrees with the district wise monthly average values, NC has experienced drought conditions numerous times in the past [13]. This observation underscores the need for multivariate modeling, emphasizing the importance of not relying solely on SPI and advocating for the inclusion of prolonged periods in drought decision-making processes [26]. To achieve these goals, we model the drought conditions using copula functions that enable identifying joint structures between variables.…”

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“…In fact, a drought period might have had an effect on groundwater levels of the examined aquifer. Particularly, the groundwater level in January of the shallow aquifer is significantly related to drought in the previous hydrological year and drought in the first trimester of the current hydrological year [59].…”