Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning
Abstract
:1. Introduction
2. Geological and Hydrogeological Settings
3. Data and Methods
3.1. Data Source
3.2. Calculation of the Standardised Drought Indices
3.3. Trend Analysis Method
3.4. Auto- and Cross-Correlation Method
3.5. Multiple Linear and Nonlinear Regressions
3.6. Artificial Neural Networks
4. Results
4.1. Changes in the Standardised Drought Indices
4.2. Time Series of Investigated Variables
4.3. Auto-Correlation and Cross-Correlation of Variables
4.4. Developed Machine Learning Models
4.4.1. Multiple Linear Regression Model
4.4.2. Multiple Nonlinear Regression Model
4.4.3. Artificial Neural Networks Model
5. Discussion
5.1. Comparison of Models
5.2. Impact on the Water Level of Vrana Lake
5.3. Limitations
5.4. Practical Implications
6. Conclusions
- The dominant no-drought conditions (SWI > 0) recorded in the previous intervals (1929–1958 and 1959–1989) were not recorded in the period 1989–2019.
- After 2006, sharp increase in temperature was noticeable, where an almost continuous series from mild hot to moderate hot years were seen.
- The MLR, MNLR, and ANN 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 result was achieved with the MNLR model for the entire trained period of 1954–2019.
- The use of a time series (long period) of historical annual data can be very interesting from the point of view of analysing the impact of current climate change on water resources, particularly when studying multiparametric systems that react very sluggishly to change.
- New water balance conditions have been established, probably by reducing underground runoff (losses) and widening the catchment area, which, with a slight increase in precipitation, has enabled the same abstraction rate and stabilization of water levels.
- The establishment of monitoring of all elements affecting the lake water level is of crucial importance for all further research including the development of a new, more reliable physical model, development of new models using machine learning, and comparisons with the results of this study.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wilhite, D.A.; Glantz, M.H. Understanding the drought phenomenon: The role of definitions. Water Int. 1985, 10, 111–120. [Google Scholar]
- Hayes, M.; Svoboda, M.; LeComte, D.; Redmond, K.; Pasteris, P. Drought monitoring: New tools for the 21st century. In Drought and Water Crises: Science, Technology, and Management Issues; CRC Press: Boca Raton, FL, USA, 2005; pp. 53–69. [Google Scholar]
- Hannaford, J. Climate-driven changes in UK river flows: A review of the evidence. Prog. Phys. Geogr. 2015, 39, 29–48. [Google Scholar] [CrossRef] [Green Version]
- Hanel, M.; Rakovec, O.; Markonis, Y.; Máca, P.; Samaniego, L.; Kyselý, J.; Kumar, R. Revisiting the recent European droughts from a long-term perspective. Sci. Rep. 2018, 8, 9499. [Google Scholar] [CrossRef] [PubMed]
- Peña-Angulo, D.; Vicente-Serrano, S.M.; Domínguez-Castro, F.; Lorenzo-Lacruz, J.; Murphy, C.; Hannaford, J.; Allan, R.P.; Tramblay, Y.; Reig-Gracia, F.; El Kenawy, A. The Complex and Spatially Diverse Patterns of Hydrological Droughts Across Europe. Water Resour. Res. 2022, 58, e2022WR031976. [Google Scholar] [CrossRef]
- McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration of time scales. In Proceedings of the Eighth Conference on Applied Climatology, American Meteorological Society, Anaheim, CA, USA, 17–23 January 1993; pp. 179–186. Available online: https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (accessed on 18 January 2022).
- Nalbantis, I.; Tsakiris, G. Assessment of Hydrological Drought Revisited. Water Resour Manag. 2009, 23, 881–897. [Google Scholar] [CrossRef]
- Palmer, W.C. U.S. Research Paper No. 45. In Meteorological Drought; US Weather Bureau: Washington, DC, USA, 1965. [Google Scholar]
- Shafer, B.A.; Dezman, L.E. Development of a Surface Water Supply Index (SWSI) to Assess the Severity of Drought Conditions in Snowpack Runoff Areas. In Proceedings of the Western Snow Conference, Fort Collins, CO, USA, 19–23 April 1982; pp. 164–175. [Google Scholar]
- Bhuiyan, C. Various drought indices for monitoring drought condition in Aravalli terrain of India. In Proceedings of the XXth ISPRS Conference International Society Photogrammetry Remote Sensing, Istanbul, Turkey, 12–23 July 2004; Available online: http://www.isprs.org/proceedings/xxxv/congress/comm7/papers/243.pdf (accessed on 18 January 2022).
- Bloomfield, J.P.; Marchant, B.P. Analysis of groundwater drought building on the standardised precipitation index approach. Hydrol. Earth Syst. Sci. 2013, 17, 4769–4787. [Google Scholar] [CrossRef] [Green Version]
- Wunsch, A.; Liesch, T.; Broda, S. Groundwater level forecasting with artificial neural networks: A comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrol. Earth Syst. Sci. 2021, 25, 1671–1687. [Google Scholar] [CrossRef]
- Zhu, S.; Hongfang, L.; Ptak, M.; Jiangyu, D.; Qingfeng, J. Lake water level fluctuation forecasting using machine learning models: A systematic review. Environ. Sci. Pollut. Res. 2020, 27, 44807–44819. [Google Scholar] [CrossRef]
- Akyuz, D.E.; Cigizoglu, H.K. Long range lake water level estimation using artificial intelligence methods. e-Zbonik Electron. Collect. Pap. Fac. Civ. Eng. 2020, 20, 1–17. Available online: https://hrcak.srce.hr/file/361707 (accessed on 18 January 2022).
- Noury, M.; Sedghi, H.; Babazedeh, H.; Fahmi, H. Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network. Water Res. 2014, 41, 261–269. [Google Scholar] [CrossRef]
- Maier, H.R.; Jain, A.; Dandy, G.C.; Sudheer, K. Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions. Environ. Model. Softw. 2010, 25, 891–909. [Google Scholar] [CrossRef]
- Rajaee, T.; Ebrahimi, H.; Nourani, V. A review of the artificial intelligence methods in groundwater level modeling. J. Hydrol. 2019, 572, 336–351. [Google Scholar] [CrossRef]
- Shen, C. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resour. Res. 2018, 54, 8558–8593. [Google Scholar] [CrossRef]
- Duan, S.; Ullrich, P.; Shu, L. Using Convolutional Neural Networks for Streamflow Projection in California. Front. Water 2020, 2, 28. [Google Scholar] [CrossRef]
- Gauch, M.; Kratzert, F.; Klotz, D.; Nearing, G.; Lin, J.; Hochreiter, S. Rainfall–Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network. Hydrol. Earth Syst. Sci. 2020, 25, 2045–2062. [Google Scholar] [CrossRef]
- Gauch, M.; Mai, J.; Lin, J. The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction. Environ. Model. Softw. 2021, 135, 104926. [Google Scholar] [CrossRef]
- Kraft, B.; Jung, M.; Körner, M.; Reichstein, M. Hybrid Modeling: Fusion of a Deep Learning Approach and a Physics-Based Model for Global Hydrological Modeling. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, 43, 1537–1544. [Google Scholar] [CrossRef]
- Kratzert, F.; Klotz, D.; Shalev, G.; Klambauer, G.; Hochreiter, S.; Nearing, G. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol. Earth Syst. Sci. 2019, 23, 5089–5110. [Google Scholar] [CrossRef] [Green Version]
- Wunsch, A.; Liesch, T.; Cinkus, G.; Ravbar, N.; Chen, Z.; Mazzilli, N.; Jourde, H.; Goldscheider, N. Karst spring discharge modeling based on deep learning using spatially distributed input data. Hydrol. Earth Syst. Sci. 2022, 26, 2405–2430. [Google Scholar] [CrossRef]
- Altunkaynak, A. Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resour. Manag. 2007, 21, 399–408. [Google Scholar] [CrossRef]
- Ondimu, S.; Murase, H. Reservoir level forecasting using neural networks: Lake Naivasha. Biosyst. Eng. 2007, 96, 135–138. [Google Scholar] [CrossRef]
- Yarar, A.; Onucyildiz, M.; Copty, N.K. Modelling level change in lakes using neuro-fuzzy and artificial neural networks. J. Hydrol. 2009, 365, 329–334. [Google Scholar] [CrossRef]
- Kisi, O.; Shiri, J.; Nikoofar, B. Forecasting daily lake levels using artificial intelligence approaches. Comput. Geosci. 2012, 41, 169–180. [Google Scholar] [CrossRef]
- Piasecki, A.; Jurasz, J.; Skowron, R. Forecasting surface water level fluctuations of lake Serwy (Northeastern Poland) by artificial neural networks and multiple linear regression. J. Environ. Eng. Landsc. Manag. 2017, 25, 379–388. [Google Scholar] [CrossRef] [Green Version]
- Guyennon, N.; Salerno, F.; Rossi, D.; Rainaldi, M.; Calizza, E.; Romano, E. Climate change and water abstraction impacts on the long-term variability of water levels in Lake Bracciano (Central Italy): A Random Forest approach. J. Hydrol. Reg. Stud. 2021, 37, 100880. [Google Scholar] [CrossRef]
- Barzkar, A.; Najafzadeh, M.; Homaei, F. Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model. Nat. Hazards 2022, 110, 1931–1952. [Google Scholar] [CrossRef]
- Basak, A.; Sakiur Rahman, A.T.M.; Das, J.; Hosono, T.; Kisi, Ö. Drought forecasting using the Prophet model in a semi-arid climate region of western India. Hydrol. Sci. J. 2022, 67, 1397–1417. [Google Scholar] [CrossRef]
- Mirboluki, A.; Mehraein, M.; Kisi, Ö. Improving accuracy of neuro fuzzy and support vector regression for drought modelling using grey wolf optimization. Hydrol. Sci. J. 2022. [Google Scholar] [CrossRef]
- Shiri, J. Modeling reference evapotranspiration in island environments: Assessing the practical implications. J. Hydrol. 2019, 570, 265–280. [Google Scholar] [CrossRef]
- Shiri, J.; Kişi, Ö.; Landeras GLópez, J.J.; Nazemi, A.H.; Stuyt, L.C.P.M. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J. Hydrol. 2012, 414–415, 302–316. [Google Scholar] [CrossRef]
- Hrnjica, B.; Bonacci, O. Lake Level Prediction using Feed Forward and Recurrent Neural Networks. Water Resour. Manag. 2019, 33, 2471–2484. [Google Scholar] [CrossRef]
- Sahoo, S.; Jha, M.K. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: A comparative assessment. Hydrogeol. J. 2013, 21, 1865–1887. [Google Scholar] [CrossRef]
- Sahoo, S.; Russo, T.A.; Elliott, J.; Foster, I. Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resour. Res. 2017, 53, 3878–3895. [Google Scholar] [CrossRef]
- Wang, Q.; Wang, S. Machine Learning-Based Water Level Prediction in Lake Erie. Water 2020, 12, 2654. [Google Scholar] [CrossRef]
- Goldscheider, N.; Chen, Z.; Auler, A.S.; Bakalowicz, M.; Broda, S.; Drew DHartmann, J.; Jiang, G.; Moosdorf, N.; Stevanovic ZVeni, G. Global distribution of carbonate rocks and karst water resources. Hydrogeol. J. 2020, 28, 1661–1677. [Google Scholar] [CrossRef] [Green Version]
- Kuhta, M.; Brkić, Ž. Seasonal Temperature Variations of Lake Vrana on the Island of Cres and Possible Influence of Global Climate Changes. J. Earth Sci. Eng. 2013, 4, 225–237. Available online: http://www.davidpublisher.com/index.php/Home/Article/index?id=4818.html (accessed on 18 January 2022).
- Stevanović, Z. Karst waters in potable water supply: A global scale overview. Environ. Earth Sci. 2019, 78, 662. [Google Scholar] [CrossRef]
- Ožanić, N. Hydrological Functioning Model of the Vrana Lake on the Cres Island. Ph.D. Thesis, University of Split, Split, Croatia, 1996. (In Croatian). [Google Scholar]
- Bonnaci, O. Analysis of variations in water levels of the Vrana Lake on the Cres island (In Croatian). Hrvat. Vode 2014, 22, 337–346. Available online: https://www.voda.hr/sites/default/files/pdf_clanka/hv_90_2014_337-346_bonacci.pdf (accessed on 18 January 2022).
- Magaš, N. Basic Geological Map of SFRY: Sheet Cres. Scale 1:100,000. L33-113; Sav. Geol. Zavod Beograd, 1968; Inst.geol.istraž: Zagreb, Croatia, 1965. [Google Scholar]
- Kuhta, M. Lake Vrana on Cres Island—Genesis, characteristics and prospects. In Proceedings of the XXXIII Congress IAH and 7th Congress ALHSUD, Zacatecas, Mexico, 11–15 October 2004. [Google Scholar]
- Ožanić, N.; Rubinić, J. Analysis of the Hydrological Regime of the Lake Vransko jezero on the Island of Cres (in Croatian). Hrvat. Vode 1994, 2, 535–543. [Google Scholar]
- Biondić, B.; Prelogović, E.; Braun, K.; Ivičić, D. Jezero Vrana Na Otoku Cresu. Hidrogeološki Istražni Radovi; Professional Report; Croatian Geological Survey: Zagreb, Croatia, 1991. (In Croatian) [Google Scholar]
- Patel, N.R.; Chopra, P.; Dadhwal, V.K. Analyzing spatial patterns of meteorological drought using standardized precipitation index. Meteorol. Appl. 2007, 14, 329–336. [Google Scholar] [CrossRef]
- Bloomfield, J.P.; Marchant, B.P.; McKenzie, A. Changes in groundwater drought associated with anthropogenic warming. Hydrol. Earth Syst. Sci. 2019, 23, 1393–1408. [Google Scholar] [CrossRef] [Green Version]
- Tigkas, D.; Vangelis, H.; Tsakiris, G. DrinC: A software for drought analysis based on drought indices. Earth Sci. Inform. 2015, 8, 697–709. [Google Scholar] [CrossRef]
- Mann, H.B. Non-parametric tests against trend. Econometrica 1945, 13, 245–259. Available online: https://www.jstor.org/stable/1907187 (accessed on 18 January 2022). [CrossRef]
- Kendall, M.G. Rank Correlation Methods; Griffin: London, UK, 1975. [Google Scholar]
- Burn, D.H.; Hag Elnur, M.A. Detection of hydrological trends and variability. J. Hydrol. 2002, 255, 107–122. [Google Scholar] [CrossRef]
- Addinsoft. XLSTAT Software, Version 2021.4; Paris, France, 2021.
- Makridakis, S.; Wheelwright, S.C.; Hyndman, R.J. Forecasting Methods and Applications, 3rd ed.; John Wiley & Sons: Singapore, 2008; p. 656. [Google Scholar]
- Seber, G.A.F.; Wild, C.J. Nonlinear Regression; John Wiley: New York, NY, USA, 2003. [Google Scholar]
- Günther, F.; Fritsch, S. NeuralNet: Training of Neural Networks. R J. 2010, 2, 30–38. [Google Scholar] [CrossRef] [Green Version]
- Riedmiller, M.; Braun, H. RPROP—A Fast Adaptive Learning Algorithm. In Proceedings of the 1992 International Symposium on Computer and Information Sciences, Antalya, Turkey, 28–30 May 1992; pp. 279–285. [Google Scholar]
- Saputra, W.; Tulus Zarlis, M.; Sembiring, R.W.; Hartama1, D. Analysis Resilient Algorithm on Artificial Neural Network Backpropagation. In Proceedings of the International Conference on Information and Communication Technology, Medan, Indonesia, 25–26 August 2017. [Google Scholar] [CrossRef]
- Ožanić, N.; Rubinić, J. The regime of inflow and runoff from Vrana Lake and the risk of permanent water pollution. RMZ-Mater. Geoenviron. 2003, 50, 281–284. [Google Scholar]
- Maier, H.R.; Dandy, G.C. Understanding the behaviour and optimizing the performance of back-propagation neural networks: An empirical study. Environ. Model. Softw. 1998, 13, 179–191. [Google Scholar] [CrossRef]
- King, M.L. The Durbin-Watson Test for Serial Correlation: Bounds for Regressions Using Monthly Data. J. Econom. 1983, 21, 357–366. [Google Scholar] [CrossRef]
- Abdulhafedh, A. How to Detect and Remove Temporal Autocorrelation in Vehicular Crash Data. J. Transp. Technol. 2017, 7, 133–147. [Google Scholar] [CrossRef] [Green Version]
- Savin, N.E.; White, K.J. The Durbin-Watson Test for Serial Correlation with Extreme Sample Sizes or Many Regressors. Econometric 1977, 45, 1989–1996. [Google Scholar] [CrossRef]
- Statistica: System Reference; StatSoft: Tulsa, OK, USA, 2001; p. 1098.
- Bonnaci, O. Preliminary Analysis of the Decrease in Water Level of Vrana Lake onthe Small Carbonate Island of Cres (Dinaric karst, Croatia). In Advances in Karst Research: Theory, Fieldwork and Applications; Parise, M., Gabrovsek, F., Kaufmann, G., Ravbar, N., Eds.; Geological Society: London, UK, 2017; p. 466. [Google Scholar] [CrossRef]
- Young, C.C.; Liu, W.C.; Hsieh, W.L. Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models. Math. Probl. Eng. 2015, 2015, 708204. [Google Scholar] [CrossRef] [Green Version]
- Zhu, S.; Hrnjica, B.; Ptak, M.; Choiński, A.; Sivakumar, B. Forecasting of water level in multiple temperate lakes using machine learning models. J. Hydrol. 2020, 585, 124819. [Google Scholar] [CrossRef]
- Choi, C.; Kim, J.; Han, H.; Han, D.; Kim, H.S. Development of Water Level Prediction Models Using Machine Learning in Wetlands: A Case Study of Upo Wetland in South Korea. Water 2020, 12, 93. [Google Scholar] [CrossRef] [Green Version]
- Sapitang, M.; Ridwan, W.M.; Kushiar, K.F.; Ahmed, A.N.; El-Shafie, A. Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy. Sustainability 2020, 12, 6121. [Google Scholar] [CrossRef]
- Shiri, J.; Kisi, O. Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J. Hydrol. 2010, 394, 486–493. [Google Scholar] [CrossRef]
- Buyukyildiz, M.; Tezel, G.; Yilmaz, V. Estimation of the Change in Lake Water Level by Artificial Intelligence Methods. Water Resour. Manag. 2014, 28, 4747–4763. [Google Scholar] [CrossRef]
- Petrik, M. Available Quantity of Water in Vrana Lake on the Cres Island (in Croatian). Građevinar 1961, 13, 93–99. [Google Scholar]
Precipitation (P) | Air Temperature (T) | Water Level (H) | Abstraction Rate (Q) | |
---|---|---|---|---|
Observation period | 1929–2019 | |||
No. of data | 91 | 39 * | 91 | 66 ** |
p-value | 0.472 | <0.0001 | <0.0001 | <0.0001 |
Kendall’s τ | 0.052 | 0.553 | −0.529 | 0.844 |
Type of trend | n.s.s. | increasing | decreasing | increasing |
Observation period | 1929–1953 | |||
No. of data | 25 | 25 | ||
p-value | 0.455 | 0.726 | ||
Kendall’s τ | −0.110 | −0.053 | ||
Type of trend | n.s.s. | n.s.s. | ||
Observation period | 1954–2019 | |||
No. of data | 66 | 39 * | 66 | 66 |
p-value | 0.486 | <0.0001 | <0.0001 | <0.0001 |
Kendall’s τ | 0.059 | 0.553 | −0.493 | 0.844 |
Type of trend | n.s.s. | increasing | decreasing | increasing |
Observation period | 1954–1990 | |||
No. of data | 37 | 10 * | 37 | 37 |
p-value | 0.724 | 0.210 | 0.013 | <0.0001 |
Kendall’s τ | −0.042 | 0.333 | −0.287 | 0.964 |
Type of trend | n.s.s. | n.s.s. | decreasing | increasing |
Observation period | 1991–2019 | |||
No. of data | 29 | 29 | 29 | 29 |
p-value | 0.358 | 0.0005 | 0.293 | <0.0001 |
Kendall’s τ | 0.123 | 0.463 | 0.141 | 0.749 |
Type of trend | n.s.s. | increasing | n.s.s. | increasing |
Observation period | 1954–1981 | |||
No. of data | 28 | 28 | 28 | |
p-value | 0.038 | 0.607 | <0.0001 | |
Kendall’s τ | 0.280 | −0.072 | 0.974 | |
Type of trend | increasing | n.s.s. | increasing | |
Observation period | 1982–1990 | |||
No. of data | 9 | 9 | 9 | 9 |
p-value | 0.466 | 0.466 | 0.001 | 0.029 |
Kendall’s τ | −0.222 | 0.222 | −0.889 | 0.611 |
Type of trend | n.s.s. | n.s.s. | decreasing | increasing |
Period | Correlation Coefficient (R) | Lag (Year) | ||||
---|---|---|---|---|---|---|
P | T | Q | P | T | Q | |
1929–2019 | 0.30 | - | - | 1 | - | - |
1954–2019 | 0.37 | −0.35 | −0.7 (±0.04) | 1 | 0 | <4 |
Training Period (1954–2004) | Testing Period (2005–2019) | Training of Entire Period (1954–2019) | |||||||
---|---|---|---|---|---|---|---|---|---|
MLR | MNLR | ANN | MLR | MNLR | ANN | MLR | MNLR | ANN | |
R2 | 0.90 | 0.93 | 0.82 | 0.42 | 0.50 | 0.43 | 0.89 | 0.96 | 0.81 |
R | 0.95 | 0.96 | 0.90 | 0.65 | 0.71 | 0.66 | 0.94 | 0.98 | 0.90 |
MAE (m) | 0.34 | 0.29 | 0.39 | 0.54 | 0.52 | 0.45 | 0.36 | 0.21 | 0.43 |
RMSE (m) | 0.41 | 0.36 | 0.55 | 0.64 | 0.70 | 0.67 | 0.44 | 0.28 | 0.57 |
SI | 0.033 | 0.029 | 0.045 | 0.057 | 0.062 | 0.060 | 0.036 | 0.023 | 0.047 |
Bias (m) | 0.00 | 0.00 | 0.00 | −0.13 | −0.02 | −0.28 | 0.00 | 0.00 | 0.00 |
Variable | Beta | p-Value |
---|---|---|
P | 0.187 | 0.0004 |
P(t−1) | 0.323 | <0.0001 |
Q(t−2) | −0.207 | 0.0004 |
H(t−1) | 0.681 | <0.0001 |
Period | Hav (m a.s.l) | Pav (mm) | Tav (°C) | Qav (×106) (m3) |
---|---|---|---|---|
1954–1981 | 13.3 | 1115.9 | 13.9 | 0.55 |
1954–1990 | 13.0 | 1076.5 | 14.3 | 0.91 |
1982–1990 | 12.1 | 954.0 | 14.4 | 2.01 |
1991–2019 | 11.2 | 1104.3 | 15.1 | 2.09 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Brkić, Ž.; Kuhta, M. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning. Sustainability 2022, 14, 10447. https://doi.org/10.3390/su141610447
Brkić Ž, Kuhta M. Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning. Sustainability. 2022; 14(16):10447. https://doi.org/10.3390/su141610447
Chicago/Turabian StyleBrkić, Željka, and Mladen Kuhta. 2022. "Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning" Sustainability 14, no. 16: 10447. https://doi.org/10.3390/su141610447