Ioannis Tsoukalas

Civil Engineer, MSc., PhD candidate
jtsoukalas@hotmail.com

Participation in research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

Published work

Publications in scientific journals

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: Ecosystem services from treated wastewater, Desalination and Water Treatment, 2017, (in press).
  2. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.
  3. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.
  4. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.

Book chapters and fully evaluated conference publications

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: ecosystem services from treated wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, National Technical University of Athens, 2016, (in press).
  2. I. Tsoukalas, P. Dimas, and C. Makropoulos, Hydrosystem optimization on a budget: Investigating the potential of surrogate based optimization techniques, 14th International Conference on Environmental Science and Technology (CEST2015), Global Network on Environmental Science and Technology, University of the Aegean, 2015.

Conference publications and presentations with evaluation of abstract

  1. Y. Moustakis, P. Kossieris, I. Tsoukalas, and A. Efstratiadis, Quasi-continuous stochastic simulation framework for flood modelling, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-534, European Geosciences Union, 2017.
  2. P. Kossieris, A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Assessing the performance of Bartlett-Lewis model on the simulation of Athens rainfall, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-8983, doi:10.13140/RG.2.2.14371.25120, European Geosciences Union, 2015.
  3. A. Efstratiadis, I. Tsoukalas, P. Kossieris, G. Karavokiros, A. Christofides, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Computational issues in complex water-energy optimization problems: Time scales, parameterizations, objectives and algorithms, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5121, doi:10.13140/RG.2.2.11015.80802, European Geosciences Union, 2015.
  4. A. Drosou, P. Dimitriadis, A. Lykou, P. Kossieris, I. Tsoukalas, A. Efstratiadis, and N. Mamassis, Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece), European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-9148, European Geosciences Union, 2015.
  5. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Handling time-expensive global optimization problems through the surrogate-enhanced evolutionary annealing-simplex algorithm, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5923, European Geosciences Union, 2015.

Details on research projects

Participation as Researcher

  1. Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system

    Duration: October 2008–November 2011

    Budget: €72 000

    Project director: N. Mamassis

    Principal investigator: D. Koutsoyiannis

    This research project includes the maintenance, upgrading and extension of the Decision Support System that developed by NTUA for EYDAP in the framework of the research project “Updating of the supervision and management of the water resources’ system for the water supply of the Athens’ metropolitan area”. The project is consisted of the following parts: (a) Upgrading of the Data Base, (b)Upgrading and extension of hydrometeorological network, (c) upgrading of the hydrometeorological data process software, (d) upgrading and extension of the Hydronomeas software, (e) hydrological data analysis and (f) support to the preparation of the annual master plans

Published work in detail

Publications in scientific journals

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: Ecosystem services from treated wastewater, Desalination and Water Treatment, 2017, (in press).

    To reduce the impact of urban effluents on the environment, strict regulatory requirements have been set up for the disposal of wastewater, in most parts of the western world, requiring treatment before disposal. At the same time, the urban environment requires water inflows to satisfy a range of urban water demands, and the corresponding water abstractions put pressure on (often scarce) water resources. A suggested synergistic solution is to use the effluents from treatment plants as an alternative resource for irrigation or for industrial uses. Despite the existence of numerous successful applications, this practice is not very common mainly because of increased capital and operational costs, usually exceeding the cost of fresh water. A possible response of the market to this drawback could be to introduce in-situ small scale treatment units to cover local water needs. In this study, we assess the benefits of such a compact wastewater treatment unit that is used to provide water for irrigating an urban green area. Apart from the aesthetic improvement, the evaporative cooling (latent heat), which reduces the air temperature, is expected to have a positive impact on thermal comfort. A pilot scheme was deployed in KEREFYT, the research centre of the Athens Water Supply and Sewerage Company (EYDAP). This scheme was simulated with the UWOT model to estimate heat fluxes and the results were fed into Energy2D (a model that simulates heat transfer) to estimate the expected temperature drop. The results are promising and suggest that these technologies could play an important role in a more sustainable, circular water economy.

    Full text: http://www.itia.ntua.gr/en/getfile/1715/1/documents/Manuscript_subm2_CM.pdf (636 KB)

  1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, Environmental Modelling and Software, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.

    In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget.

    Related works:

    • [11] Early presentation if EGU conference

    Full text: http://www.itia.ntua.gr/en/getfile/1587/2/documents/SEEAS_paper.pdf (4310 KB)

    Additional material:

    Other works that reference this work (this list might be obsolete):

    1. Dariane , A. B., and M. M. Javadianzadeh, Towards an efficient rainfall–runoff model through partitioning scheme, Water, 8, 63; doi:10.3390/w8020063, 2016.
    2. Yaseen, Z. M., O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Quilty, and A. El-Shafie, Boost stream-flow forecasting model with extreme learning machine data-driven: A case study in a semi-arid region in Iraq, Journal of Hydrology, doi:10.1016/j.jhydrol.2016.09.035, 2016.
    3. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
    4. #Christelis, V., V. Bellos, and G. Tsakiris, Employing surrogate modelling for the calibration of a 2D flood simulation model, Sustainable Hydraulics in the Era of Global Change: Proceedings of the 4th IAHR Europe Congress (Liege, Belgium, 27-29 July 2016), A. S. Erpicum, M. Pirotton, B. Dewals, P. Archambeau (editors), CRC Press, 2016.

  1. I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015.

    Operation of large-scale hydropower reservoirs is a complex problem that involves conflicting objectives, such as hydropower generation and water supply. Deriving optimal operational rules is a challenging task due to the non-linearity of the system dynamics and the uncertainty of future inflows and water demands. A common approach to derive optimal control policies is to couple simulation models with optimization algorithms. This paper in order to investigate the performance of a future reservoir and safely infer about its significance employs stochastic simulation, thus long synthetically generated time-series and a multi-objective version of the Parameterization-Simulation-Optimization (PSO) framework to develop uncertainty-aware operational rules. Furthermore, in order to handle the high computational effort that ensues from that coupling we investigate the potential of a surrogate-based multi-objective optimization algorithm, ParEGO. The PSO framework is deployed with WEAP21 water resources management model as simulation engine and MATLAB for the implementation of optimization algorithms. A comparison between NSGAII and ParEGO optimization algorithms is performed to assess the effectiveness of the proposed algorithm. The aforementioned comparison showed that ParEGO provides efficient approximations of the Pareto front while reducing the computational effort required. Finally, the potential benefit and the significance of the future reservoir is underlined.

    Full text: http://www.itia.ntua.gr/en/getfile/1569/1/documents/tsoukalas_WRM.pdf (2008 KB)

    Other works that reference this work (this list might be obsolete):

    1. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.

  1. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.

    Developing long term operation rules for multi-reservoir systems is complicated due to the number of decision variables, the non-linearity of system dynamics and the hydrological uncertainty. This uncertainty can be addressed by coupling simulation models with multi-objective optimisation algorithms driven by stochastically generated hydrological timeseries but the computational effort required imposes barriers to the exploration of the solution space. The paper addresses this by (a) employing a parsimonious multi-objective parameterization-simulation-optimization (PSO) framework, which incorporates hydrological uncertainty through stochastic simulation and allows the use of probabilistic objective functions and (b) by investigating the potential of multi-objective surrogate based optimisation (MOSBO) to significantly reduce the resulting computational effort. Three MOSBO algorithms are compared against two multi-objective evolutionary algorithms. Results suggest that MOSBOs are indeed able to provide robust, uncertainty-aware operation rules much faster, without significant loss of neither the generality of evolutionary algorithms nor of the knowledge embedded in domain-specific models.

    Other works that reference this work (this list might be obsolete):

    1. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.

Book chapters and fully evaluated conference publications

  1. E. Rozos, I. Tsoukalas, K. Ripis, E. Smeti, and C. Makropoulos, Turning black into green: ecosystem services from treated wastewater, 13th IWA Specialized Conference on Small Water and Wastewater Systems, Athens, Greece, National Technical University of Athens, 2016, (in press).

    In order to reduce the impact of the urban effluents on the environment, modern societies have imposed restrictions regarding the quality of the disposals. For this reason, in the majority of the western world cities, the wastewater is treated before disposal. However, on the other side of the urban water cycle, water abstractions keep putting an increasing pressure on the water resources. As a countermeasure, treated wastewater is used occasionally as an alternative resource by employing large scale infrastructure to treat and supply water for either irrigation or industrial uses. Despite the existence of numerous successful applications, this practice is not very common mainly because of the increased capital and operational costs, usually exceeding the cost of fresh water. The response of the market to this drawback was to introduce in-situ small scale treatment units to cover local water needs. In this study, we assess the benefits of a compact wastewater treatment unit that is used to provide water for irrigating a green area. Apart from the aesthetic improvement, benefits are expected because of the evaporative cooling (latent heat), which reduce the air temperature. A pilot scheme was set up in KEREFYT, the research centre of Athens water supply company. This scheme was simulated with UWOT model to estimate the heat fluxes and the results were fed into Energy2D (a model that simulates heat transfer) to estimate the expected temperature drop.

    Full text: http://www.itia.ntua.gr/en/getfile/1600/1/documents/Manuscript_QiNArbH.pdf (509 KB)

  1. I. Tsoukalas, P. Dimas, and C. Makropoulos, Hydrosystem optimization on a budget: Investigating the potential of surrogate based optimization techniques, 14th International Conference on Environmental Science and Technology (CEST2015), Global Network on Environmental Science and Technology, University of the Aegean, 2015.

    Development of uncertainty-aware operational rules for multi-reservoir systems is a demanding and challenging task due to the complexity of the system dynamics, the number of decision variables and the hydrological uncertainty. In order to overcome this issue the parsimonious parameterization-simulation-optimization (PSO) framework is employed coupled with stochastically generated hydrological time-series. However, when the simulation model requires long computational time this coupling imposes a computational barrier to the framework. The purpose of this paper is threefold: a) Investigate the potential of Efficient Global Optimization (EGO) algorithm (and its variants) which is capable of reaching global optima within a few simulation model evaluations (~500 or less). b) Extend the capabilities of WEAP21 water resources management model by using it within PSO framework (named WEAP21-PSO) and c) Validate and compare the results of WEAP21-PSO using the well-known hydrosystem management model Hydronomeas coupled with Evolutionary Annealing Simplex (EAS) optimization algorithm. Results confirm that EGO has the potential and the capabilities to handle computationally demanding problems and furthermore is capable of locating the optimal solution within few simulation model evaluations and that the WEAP21-PSO framework performs well at the task at hand.

    Full text: http://www.itia.ntua.gr/en/getfile/1574/1/documents/cest2015_00162_oral_paper.pdf (475 KB)

    See also: http://cest.gnest.org/cest15proceedings/public_html/papers/cest2015_00162_oral_paper.pdf

Conference publications and presentations with evaluation of abstract

  1. Y. Moustakis, P. Kossieris, I. Tsoukalas, and A. Efstratiadis, Quasi-continuous stochastic simulation framework for flood modelling, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-534, European Geosciences Union, 2017.

    Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event. In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS), while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall. This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

    Full text: http://www.itia.ntua.gr/en/getfile/1680/2/documents/FINAL_Moustakis_EGU2017.pdf (1492 KB)

    Additional material:

  1. P. Kossieris, A. Efstratiadis, I. Tsoukalas, and D. Koutsoyiannis, Assessing the performance of Bartlett-Lewis model on the simulation of Athens rainfall, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-8983, doi:10.13140/RG.2.2.14371.25120, European Geosciences Union, 2015.

    Many hydrological applications require the use of long rainfall data across a wide range of fine time scales. To meet this necessity, stochastic approaches are usually employed for the generation of large number of rainfall events, following a Monte Carlo approach. In this framework, Bartlett-Lewis model (BL) is a key representative from the family of Poisson-cluster stochastic processes. Here, we examine the performance of three different versions of BL model, with number of parameters varying from 5 up to 7, in representing the characteristics of convective and frontal rainfall of Athens (Greece). Apart from the typical statistical characteristics that are explicitly preserved by the stochastic model (mean, variance, lag-1 autocorrelation, probability dry), we also attempt to preserve the statistical distribution of annual rainfall maxima, as well as two important temporal properties of the observed storm events, i.e. the duration of storms and the time distance between subsequent events. This task is not straightforward, given that these characteristics are not described in the theoretical equations of the model, but they should be empirically evaluated on the basis of synthetic data. The analysis is conducted on monthly basis and for multiple time scales, i.e. from hourly to daily. Further to that, we focus on the formulation of the calibration problem, by assessing the performance of the BL model against issues such as choice of statistics to preserve, time scales, distance metrics, etc.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.2.14371.25120

  1. A. Efstratiadis, I. Tsoukalas, P. Kossieris, G. Karavokiros, A. Christofides, A. Siskos, N. Mamassis, and D. Koutsoyiannis, Computational issues in complex water-energy optimization problems: Time scales, parameterizations, objectives and algorithms, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5121, doi:10.13140/RG.2.2.11015.80802, European Geosciences Union, 2015.

    Modelling of large-scale hybrid renewable energy systems (HRES) is a challenging task, for which several open computational issues exist. HRES comprise typical components of hydrosystems (reservoirs, boreholes, conveyance networks, hydropower stations, pumps, water demand nodes, etc.), which are dynamically linked with renewables (e.g., wind turbines, solar parks) and energy demand nodes. In such systems, apart from the well-known shortcomings of water resources modelling (nonlinear dynamics, unknown future inflows, large number of variables and constraints, conflicting criteria, etc.), additional complexities and uncertainties arise due to the introduction of energy components and associated fluxes. A major difficulty is the need for coupling two different temporal scales, given that in hydrosystem modeling, monthly simulation steps are typically adopted, yet for a faithful representation of the energy balance (i.e. energy production vs. demand) a much finer resolution (e.g. hourly) is required. Another drawback is the increase of control variables, constraints and objectives, due to the simultaneous modelling of the two parallel fluxes (i.e. water and energy) and their interactions. Finally, since the driving hydrometeorological processes of the integrated system are inherently uncertain, it is often essential to use synthetically generated input time series of large length, in order to assess the system performance in terms of reliability and risk, with satisfactory accuracy. To address these issues, we propose an effective and efficient modeling framework, key objectives of which are: (a) the substantial reduction of control variables, through parsimonious yet consistent parameterizations; (b) the substantial decrease of computational burden of simulation, by linearizing the combined water and energy allocation problem of each individual time step, and solve each local sub-problem through very fast linear network programming algorithms, and (c) the substantial decrease of the required number of function evaluations for detecting the optimal management policy, using an innovative, surrogate-assisted global optimization approach.

    Full text:

    See also: http://dx.doi.org/10.13140/RG.2.2.11015.80802

  1. A. Drosou, P. Dimitriadis, A. Lykou, P. Kossieris, I. Tsoukalas, A. Efstratiadis, and N. Mamassis, Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece), European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-9148, European Geosciences Union, 2015.

    We present a multi-criteria simulation-optimization framework for the optimal design and setting of flood protection structures along river banks. The methodology is tested in the lower course of the Arachthos River (Epirus, Greece), downstream of the hydroelectric dam of Pournari. The entire study area is very sensitive, particularly because the river crosses the urban area of Arta, which is located just after the dam. Moreover, extended agricultural areas that are crucial for the local economy are prone to floods. In the proposed methodology we investigate two conflicting criteria, i.e. the minimization of flood hazards (due to damages to urban infrastructures, crops, etc.) and the minimization of construction costs of the essential hydraulic structures (e.g. dikes). For the hydraulic simulation we examine two flood routing models, named 1D HEC-RAS and quasi-2D LISFLOOD, whereas the optimization is carried out through the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the EAS method.

    Full text:

  1. I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Handling time-expensive global optimization problems through the surrogate-enhanced evolutionary annealing-simplex algorithm, European Geosciences Union General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna, EGU2015-5923, European Geosciences Union, 2015.

    In water resources optimization problems, the calculation of the objective function usually presumes to first run a simulation model and then evaluate its outputs. In several cases, however, long simulation times may pose significant barriers to the optimization procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required by the problem’s complexity. A promising novel strategy to address these shortcomings is the use of surrogate modelling techniques within global optimization algorithms. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modelling with the effectiveness and efficiency of the EAS method. The algorithm combines three different optimization approaches (evolutionary search, simulated annealing and the downhill simplex search scheme), in which key decisions are partially guided by numerical approximations of the objective function. The performance of the proposed algorithm is benchmarked against other surrogate-assisted algorithms, in both theoretical and practical applications (i.e. test functions and hydrological calibration problems, respectively), within a limited budget of trials (from 100 to 1000). Results reveal the significant potential of using SE-EAS in challenging optimization problems, involving time-consuming simulations.

    Full text: