Investigating lake drought prevention using a DRL-based method
Keywords:
Drought simulation, Mead Lake, Entity Component System, Deep reinforcement learningAbstract
Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes is of great importance and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from ground water and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of scenario-based design and optimal strategy selection. For optimal strategy selection a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.
References
1. Moghim, S., & Rahmani, J. (2021). Efficient Water Management under Climate Change in Urmia Plain. JWSS-Isfahan University of Technology, 25(1), 129-140.
2. Kashani, A. R., Camp, C. V., Rostamian, M., Azizi, K., & Gandomi, A. H. (2022). Population-based optimization in structural engineering: a review. Artificial Intelligence Review, 1-108.
3. Bazmara, M., Silani, M., & Dayyani, I. (2021). Effect of functionally-graded interphase on the elasto-plastic behavior of nylon-6/clay nanocomposites; a numerical study. Defence Technology, 17(1), 177-184. https://doi.org/10.1016/j.dt.2020.03.003
4. Partohaghighi, M., & Akgül, A. (2021). Modelling and simulations of the SEIR and Blood Coagulation systems using Atangana-Baleanu-Caputo derivative. Chaos, Solitons & Fractals, 150, 111135. https://doi.org/10.1016/j.chaos.2021.111135
5. Mianroodi, M., Altmeyer, G., & Touchal, S. (2019). Experimental and numerical FEM-based determinations of forming limit diagrams of St14 mild steel based on Marciniak-Kuczynski model. Journal of Mechanical Engineering and Sciences, 13(4), 5818-5831. https://doi.org/10.15282/jmes.13.4.2019.08.0464
6. Vaheddoost, B., Fathian, F., Gul, E., & Safari, M. J. S. (2022). Studying the Changes in the Hydro‐Meteorological Components of Water Budget in Lake Urmia. Water Resources Research, 58(7), e2022WR032030. https://doi.org/10.1029/2022WR032030
7. Abbasian, M. S., Abrishamchi, A., Najafi, M. R., & Moghim, S. (2020). Multi-site statistical downscaling of precipitation using generalized hierarchical linear models: a case study of the imperilled Lake Urmia basin. Hydrological Sciences Journal, 65(14), 2466-2481. https://doi.org/10.1080/02626667.2020.1810255
8. Laird, K. R., Kingsbury, M. V., & Cumming, B. F. (2010). Diatom habitats, species diversity and water-depth inference models across surface-sediment transects in Worth Lake, northwest Ontario, Canada. Journal of Paleolimnology, 44(4), 1009-1024. https://doi.org/10.1007/s10933-010-9470-0
9. Vanderkelen, I., van Lipzig, N. P., & Thiery, W. (2018). Modelling the water balance of Lake Victoria (East Africa)<? xmltexbreak?>–Part 1: Observational analysis. Hydrology and Earth System Sciences, 22(10), 5509-5525. https://doi.org/10.5194/hess-22-5509-2018
10. Bazmara, M., Mianroodi, M., & Silani, M. (2023). Application of Physics-informed neural networks for nonlinear buckling analysis of beams. Acta Mechanica Sinica. https://doi.org/10.1007/s10409-023-22438-x
11. Abbasian, M., Moghim, S., & Abrishamchi, A. (2019). Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135, 1465-1483. https://doi.org/10.1007/s00704-018-2456-y
12. Merritt, M. L., & Konikow, L. F. (2000). Documentation of a computer program to simulate lake-aquifer interaction using the MODFLOW ground-water flow model and the MOC3D solute-transport model (No. 4167). US Department of the Interior, US Geological Survey. https://doi.org/10.3133/wri004167
13. Singh, H., Najafi, M. R., & Cannon, A. (2022). Evaluation and joint projection of temperature and precipitation extremes across Canada based on hierarchical Bayesian modelling and large ensembles of regional climate simulations. Weather and Climate Extremes, 36, 100443. https://doi.org/10.1016/j.wace.2022.100443
14. Ghayoomi, H., Laskey, K., Miller-Hooks, E., Hooks, C., & Tariverdi, M. (2021). Assessing Resilience of Hospitals to Cyberattack. Digital Health, 7, 1–15. https://doi.org/10.1177/20552076211059366
15. Shahverdi, B., Miller-Hooks, E., Tariverdi, M., Ghayoomi, H., Prentiss, D., & Kirsch, T. D. (2022). Models for assessing strategies for improving hospital capacity for handling patients during a pandemic. Disaster medicine and public health preparedness, 1-10. https://doi.org/10.1017/dmp.2022.12
16. Ghayoomi, H., Miller-Hooks, E., Tariverdi, M., Shortle, J., & Kirsch, T. D. (2022). Maximizing hospital capacity to serve pandemic patient surge in hot spots via queueing theory and microsimulation. IISE Transactions on Healthcare Systems Engineering, 1-19. https://doi.org/10.1080/24725579.2022.2149936
17. Shahverdi, B., Ghayoomi, H., Miller-Hooks, E., Tariverdi, M., & Kirsch, T. D. (2022, December). Regional maximum hospital capacity estimation for Covid-19 pandemic patient care in surge through simulation. In 2022 Winter Simulation Conference (WSC) (pp. 508-520). IEEE.
18. Cuttone, A., Lehmann, S., & Larsen, J. E. (2016). Geoplotlib: a python toolbox for visualizing geographical data. arXiv preprint arXiv:1608.01933. https://doi.org/10.48550/arXiv.1608.01933
19. Carlowicz, M. (2022, July 20). Lake Mead Keeps Dropping [Image report]. Earth Observatory; NASA Earth Observatory. https://earthobservatory.nasa.gov/images/150111/lake-mead-keeps-dropping
20. Hatledal, L. I., Chu, Y., Styve, A., & Zhang, H. (2021). Vico: An entity-component-system based co-simulation framework. Simulation Modelling Practice and Theory, 108, 102243. https://doi.org/10.1016/j.simpat.2020.102243
21. The United States Geological Survey (USGS) team. (2022, August 30). USGS Links for HUC 15010005—Lake Mead. The United States Geological Survey (USGS) Reports. https://water.usgs.gov/lookup/getwatershed?15010005
22. National Water National Water Dashboard. (2022, August 30). USGS | National Water Dashboard. https://dashboard.waterdata.usgs.gov
23. Eltahir, E. A., & Bras, R. L. (1996). Precipitation recycling. Reviews of geophysics, 34(3), 367-378. https://doi.org/10.1029/96RG01927
24. De Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M. P., Frappart, F., Calmant, S., & Bulhões Mendes, C. A. (2013). Large‐scale hydrologic and hydrodynamic modeling of the Amazon River basin. Water Resources Research, 49(3), 1226-1243. https://doi.org/10.1002/wrcr.20067
25. Ji, Z. G. (2017). Hydrodynamics and water quality: modeling rivers, lakes, and estuaries. John Wiley & Sons.
26. Okhravi, S., Eslamian, S., & Esfahany, S. T. (2017). Drought in Lake Urmia. In Handbook of Drought and Water Scarcity. CRC Press.
27. Knutson, C. L. (2008). The role of water conservation in drought planning. Journal of soil and water conservation, 63(5), 154A-160A. https://doi.org/10.2489/jswc.63.5.154A
28. Ruiz, D. M., Tallis, H., Tershy, B. R., & Croll, D. A. (2020). Turning off the tap: Common domestic water conservation actions insufficient to alleviate drought in the United States of America. PloS one, 15(3), e0229798. https://doi.org/10.1371/journal.pone.0229798
29. Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3), 279–292. https://doi.org/10.1007/BF00992698
