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Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning

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  • Željka Brkić

    (Croatian Geological Survey, Department of Hydrogeology and Engineering Geology, 10000 Zagreb, Croatia)

  • Mladen Kuhta

    (Croatian Geological Survey, Department of Hydrogeology and Engineering Geology, 10000 Zagreb, Croatia)

Abstract

Vrana Lake on the karst island of Cres (Croatia) is the largest freshwater lake in the Mediterranean islands. The lake cryptodepression, filled with 220 million m 3 of fresh drinking water, represents a specific karst phenomenon. To better understand the impact of water level change drivers, the occurrence of meteorological and hydrological droughts was analysed. Basic machine learning methods (ML) such as the multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) were used to simulate water levels. Modelling was carried out considering annual inputs of precipitation, air temperature, and abstraction rate as well as their influential lags which were determined by auto-correlation and cross-correlation techniques. Hydrological droughts have been recorded since 1986, and after 2006 a series of mostly mild hot to moderate hot years was recorded. All three ML models have been trained to recognize extreme conditions in the form of less precipitation, high abstraction rate, and, consequently, low water levels in the testing (predicting) period. The best statistical indicators were achieved with the MNLR model. The methodologies applied in the study were found to be useful tools for the analysis of changes in water levels. Extended monitoring of water balance elements should precede any future increase in the abstraction rate.

Suggested Citation

  • Željka Brkić & Mladen Kuhta, 2022. "Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10447-:d:894762
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    References listed on IDEAS

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