Assistant Professor, Hydrology and Hydrodynamics Department

Institute of Geophysics Polish Academy of Sciences
addres: Księcia Janusza 64, 01-452 Warsaw
phone: +48 22 6915-858, e-mail: adampp@igf.edu.pl

Education

2024 – professor of exact and natural sciences in the discipline of Earth and environmental science
2014 – habilitation in Earth Sciences, Institute of Geophysics Polish Academy of Sciences
2006 – Ph.D. thesis, Institute of Geophysics Polish Academy of Sciences
2001 – MSc., Faculty of Geography and Regional Studies, University of Warsaw

Employment

since 2006 -  Assistant Professor, Institute of Geophysics Polish Academy of Sciences

Awards

• 2017 – II Award for scientific publications, Institute of Geophysics Polish Academy of Sciences
• 2016 – I Award for scientific publications, Institute of Geophysics Polish Academy of Sciences
• 2010 – Kacper Rybicki Scholarship for young scientists, Institute of Geophysics Polish Academy of Sciences
• 2009 – Prolongation of Start Scholarship for young scientists, Foundation for Polish Science
• 2008 – Start Scholarship for young scientists, Foundation for Polish Science
• 2003 – Best young scientists presentation at XXIII Polish School of Hydraulics

Research interests

• Rainfall-runoff modelling
• Water temperature modelling in streams
• Evaluation of longitudinal dispersion coefficients in natural rivers
• Evolutionary Algorithms
• Differential Evolution
• Swarm Intelligence
• Particie Swarm Optimization
• Metaheuristics
• Artificial Neural Networks

Research grants:

• 2017-2020 – Principal investigator of the research project funded by National Science Centre nr 2016/21/B/ST10/02516 „ Impact of expected climate change on water temperatures of selected Polish rivers”; more information is available at https://ecc-wt.igf.edu.pl/
• 2013-2015 – Principal investigator of the research project for young scientists Iuventus Plus nr IP2012040672 „Forecasting water temperature in rivers by means of empirical models”, founded by the Ministry of Science and Higher Education of Poland
• 2013-2014 – Principal investigator of the research project for young scientists founded by Institute of Geophysics Polish Academy of Sciences nr 1b/IGFPAN/2012/MŁ “Development of optimization methods for deterministic catchment runoff models for their application in temperate climate zones”
• 2005-2006 – Ph.D. grant nr 2 P04D 008 29, „Inteligent analysis of hydrological data” founded by Institute of Geophysics Polish Academy of Sciences

Editorial Board

since 2016 - Associate Editor of Swarm and Evolutionary Computation journal, https://www.journals.elsevier.com/swarm-and-evolutionary-computation/

Publications on Web of Science lists

• Piccolroaz, S, Zhu S, Ladwig R, Carrea L, Oliver S, Piotrowski AP, Ptak M, Shinohara R, Sojka M, Woolway RI, Zhu DZ, 2024. Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects. Reviews of Geophysics 62(1), e2023RG000816, https://doi.org/10.1029/2023RG000816
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2024. To what extent evolutionary algorithms can benefit from a longer search?, Information Sciences, 654, 119766, https://doi.org/10.1016/j.ins.2023.119766
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2023. Choice of benchmark optimization problems does matter, Swarm and Evolutionary Computation, 83, 101378, https://doi.org/10.1016/j.swevo.2023.101378
• Napiorkowski JJ, Piotrowski AP, Karamuz E, Senbeta TB, 2023. Calibration of conceptual rainfall‑runoff models by selected differential evolution and particle swarm optimization variants, Acta Geophysica, 71, 2325–2338, https://doi.org/10.1007/s11600-022-00988-0
• Zhu S, Ptak M, Sojka M, Piotrowski AP, Luo W, 2023. A simple approach to estimate lake surface water temperatures in Polish lowland lakes, Journal of Hydrology: Regional Studies, 48, 101468, https://doi.org/10.1016/j.ejrh.2023.101468
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2023. Particle Swarm Optimization or Differential Evolution — A comparison, Engineering Applications of Artificial Intelligence, 121, 106008, doi:10.1016/j.engappai.2023.106008
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2023. How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?  IEEE Access, 11, 19775--19793, doi:10.1109/access.2023.3247954
• Piotrowski AP, Napiorkowski JJ, Zhu S, 2023. Novel air2water model variant for lake surface temperature modelling with detailed analysis of calibration methods, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,16, 553-569, doi:10.1109/JSTARS.2022.3226516, 2021 JIF=4.715.
• Piotrowski AP, Zhu S, Napiorkowski JJ, 2022. Air2water model with nine parameters for lake surface temperature assessment. Limnologica, 94, 125967, doi:10.1016/j.limno.2022.125967, 2020 JIF=2.093
• Piotrowski AP, Piotrowska AE, 2022. Differential evolution and particle swarm optimization against COVID-19, Artificial Intelligence Review, doi:10.1007/s10462-021-10052-w.
• Piotrowski AP, Osuch M, Napiorkowski JJ, 2021. Influence of the choice of stream temperature model on the projections of water temperature in rivers Jornal of Hydrology, 601, 126629, doi:10.1016/j.jhydrol.2021.126629, 2020 JIF=5.722.
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2021. Input dropout in product unit neural networks for stream water temperature modelling. Jornal of Hydrology, 598, 126253, doi:10.1016/j.jhydrol.2021.126253, 2020 JIF=5.722.
• Zhu S, Piotrowski AP, Ptak M, Napiorkowski JJ, Dai J, Ji Q, 2021. How does the calibration method impact the performance of the air2water model for the forecasting of lake surface water temperatures? Jornal of Hydrology, 597, 126219, doi:10.1016/j.jhydrol.2021.126219, 2020 JIF=5.722.
• Zhu SL, Piotrowski AP, 2020. River/stream water temperature forecasting using artificial intelligence models: a systematic review. Acta Geophysica, 68, 5, 1433-1442
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2020. Population size in Particle Swarm Optimization. Swarm and Evolutionary Computation, 58, 100718, doi:10.1016/j.swevo.2020.100718, IF=6.330, 5YIF=6.242
• Piotrowski AP, Napiorkowski JJ, Piotrowska AE, 2020. Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling. Earth-Science Reviews, 201, 103076, doi:10.1016/j.earscirev.2019.103076, IF=9.530, 5YIF=10.640
• Piotrowski AP, Osuch M, Napiorkowski JJ, 2019. Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations. Water Resources Management, 33, 4509–4524, doi:10.1007/s11269-019-02368-8, IF=2.987, 5YIF=3.256.
• Piotrowski AP, Napiorkowski JJ, 2019. Simple modifications of the nonlinear regression stream temperature model for daily data. Journal of Hydrology, 572, 308-328, doi:10.1016/j.jhydrol.2019.02.035, IF=4.405, 5YIF=4.938.
• Piotrowski AP, Napiorkowski JJ, Osuch M, 2019. Relationship Between Calibration Time and Final Performance of Conceptual Rainfall-Runoff Models. Water Resources Management, 33, 19-37, doi:10.1007/s11269-018-2085-3, IF=2.987. 5YIF=3.256.
• Napiorkowski JJ, Piotrowski AP, 2018. Hydrodynamiczne metody wyprowadzenia parametrów modelu Muskingum. (Methods of hydrodynamic derivation of parameters of the Muskingum model), III Krajowy Kongres Hydrologiczny, Monografie Komitetu Gospodarki Wodnej, 41, 75–89, ISSN 0867-7816.
• Piotrowski AP, Napiorkowski JJ, 2018. Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method. Journal of Hydrology, 561, 395–412, doi:10.1016/j.jhydrol.2018.04.016, IF=3.727, 5YIF=4.314.
• Piotrowski, A.P. (2018) Across Neighborhood Search algorithm: A comprehensive analysis. Information Sciences, 435, 334-381, https://doi.org/10.1016/j.ins.2018.01.004
• Piotrowski, A.P., Napiorkowski, J.J. (2018) Some metaheuristics should be simplified. Information Sciences, 427, 32-62, https://doi.org/10.1016/j.ins.2017.10.039
• Piotrowski, A.P., Napiorkowski M.J., Napiorkowski J.J., Rowinski, P.M.(2017) Swarm Intelligence and Evolutionary Algorithms: Performance versus speed. Information Science 384, 34-85, http://dx.doi.org/10.1016/j.ins.2016.12.028
• Piotrowski, A.P. (2017) Review of Differential Evolution population size. Swarm and Evolutionary Computation 32, 1-24, http://dx.doi.org/10.1016/j.swevo.2016.05.003
• Piotrowski, A.P., Napiorkowski, M.J., Napiorkowski, J.J., Osuch, M., Kundzewicz, Z.W. (2017) Are modern metaheuristics successful in calibrating simple conceptual rainfall-runoff models? Hydrological Sciences Journal 62(4), 606-625, http://dx.doi.org/10.1080/02626667.2016.1234712
• Piotrowski, A.P., Napiorkowski, M.J. (2016) May the same numerical optimizer be used when searching either for the best or for the worst solution to a real-world problem? Information Sciences 373, 124-148, http://dx.doi.org/10.1016/j.ins.2016.08.057
• Piotrowski, A.P., Napiorkowski, J.J. (2016) Searching for structural bias in particle swarm optimization and differential evolution algorithms. Swarm Intelligence 10(4), 307-353, http://dx.doi.org/10.1007/s11721-016-0129-y
• Piotrowski, A.P., Napiorkowski, J.J., Osuch, M., Napiorkowski, M.J.(2016) On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks.Hydrological Sciences Journal 61(10), 1903-1925, http://dx.doi.org/10.1080/02626667.2015.1085650
• Piotrowski, A.P., Napiorkowski, M.J., Kalinowska, M., Napiorkowski, J.J., Osuch, M. (2016) Are Evolutionary Algorithms effective in calibrating different Artificial Neural Network types for streamwater temperature prediction? Water Resources Management 30, 1217-1237, http://dx.doi.org/10.1007/s11269-015-1222-5
• Piotrowski, A.P., Napiorkowski, M.J., Napiorkowski, J.J., Osuch, M.(2015) Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology 529, 302-315, http://dx.doi.org/10.1016/j.jhydrol.2015.07.044
• Piotrowski, A.P. (2015) Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Information Sciences 297, 191-201, http://dx.doi.org/10.1016/j.ins.2014.11.023
• Piotrowski, A.P. (2014) Differential Evolution algorithms applied to Neural Network training suffer from stagnation. Applied Soft Computing 21, 382-406, http://dx.doi.org/10.1016/j.asoc.2014.03.039
• Piotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M. (2014) How novel is the ‘‘novel’’ black hole optimization approach? Information Sciences 267, 191-200, http://dx.doi.org/10.1016/j.ins.2014.01.026
• Piotrowski, A.P., Osuch, M., Napiorkowski, M.J., Rowiński, P.M., Napiorkowski, J.J. (2014) Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river. Computers & Geoscences 64, 136-151, http://dx.doi.org/10.1016/j.cageo.2013.12.013
• Piotrowski, A.P. (2013) Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators. Information Sciences 241, 164-194, http://dx.doi.org/10.1016/j.ins.2013.03.060
• Piotrowski, A.P., Napiorkowski, J.J. (2013) A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modeling. Journal of Hydrology 476, 97-111, http://dx.doi.org/10.1016/j.jhydrol.2012.10.019
• Piotrowski, A.P., Napiorkowski, J.J. (2012) Product-Units neural networks for catchment runoff forecasting. Advances in Water Resources 49, 97-113, http://dx.doi.org/10.1016/j.advwatres.2012.05.016
• Piotrowski, A.P., Napiorkowski, J.J., Kiczko, A. (2012) Corrigendum to:‘‘Differential evolution algorithm with separated groups for multi-dimensional optimization problems’’ [Eur. J. Oper. Res. 216 (2012) 33–46]. European Journal of Operational Research 219(2), 488, http://dx.doi.org/10.1016/j.ejor.2011.12.043
• Piotrowski, A.P., Napiorkowski, J.J., Kiczko, A. (2012) Differential Evolution algorithm with separated groups for multi-dimensional optimization problems. European Journal of Operational Research 216, 33-46, http://dx.doi.org/10.1016/j.ejor.2011.07.038
• Piotrowski, A.P., Rowinski, P.M., Napiorkowski, J.J. (2012) Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers. Expert Systems with Applications 39, 1354-1361, http://dx.doi.org/10.1016/j.eswa.2011.08.016
• Piotrowski, A.P., Napiorkowski, J.J. (2011) Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus Levenberg –Marquardt approach. Journal of Hydrology 407, 12-27, http://dx.doi.org/10.1016/j.jhydrol.2011.06.019
• Piotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M., Wallis, S.G.(2011) Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents. Hydrological Sciences Journal 56(5), 883-894, http://dx.doi.org/10.1080/02626667.2011.583398
• Piotrowski, A.P., Napiorkowski, J.J. (2010) Grouping differential evolution algorithm for multi-dimensional optimization problems. Control and Cybernetics 39(2), 527-550, http://control.ibspan.waw.pl:3000/contents/show/19?year=2010
• Rowiński, P.M. and Piotrowski, A. (2008) Estimation of parameters of transient storage model by means of multi-layer perceptron neural networks, Hydrological Sciences Journal 53(1), 165-178, http://www.tandfonline.com/doi/pdf/10.1623/hysj.53.1.165
• Piotrowski, A., Wallis, S.G., Napiórkowski, J.J. and Rowiński, P.M.(2007) Evaluation of 1-D tracer concentration profile in a small river by means of multi-layer perceptron neural networks, Hydrology and Earth System Sciences 11, 1883-1896, www.hydrol-earth-syst-sci.net/11/1883/2007/
• Piotrowski, A., Napiórkowski, J.J., Rowiński, P.M. (2006) Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study, Nonlinear Processes in Geophysics 13, 443-448, www.nonlin-processes-geophys.net/13/443/2006/
• Rowiński, P.M., Piotrowski, A., Napiórkowski, J.J. (2005) Are artificial neural networks techniques relevant for the estimates of longitudinal dispersion coefficient in rivers?, Hydrological Sciences Journal 50(1), 175-187, http://www.tandfonline.com/doi/pdf/10.1623/hysj.50.1.175.56339

Other publication

• Napiorkowski M., Piotrowski, A.P., Napiorkowski J.J. (2014) Stream temperature forecasting by means of ensemble of neural networks: Importance of input variables and ensemble size. Proceedings of the River Flow 2014, 03-05 September, Lausanne, Switzerland.
• Napiorkowski J.J., Piotrowski A., Rowinski P.M., Wallis S.G. (2012) Product Unit neural networks for estimations of longitudinal dispersion coefficients in rivers. 2nd IAHR Europe Congress, 27-29 June, Germany, Munich.
• Piotrowski, A.P. (2011) Pure and Applied Geophysics 168, 1899-1900; DOI 10.1007/s00024-010-0252-4: Book Review: Numerical Modeling in Open Channel Hydraulics, by R. Szymkiewicz. Water Science and Technology Library, Vol. 83, Springer, 2010, ISBN: 978-90-481-3673-5 (hardback); e-ISBN: 978-90-481-3674-2;
• Piotrowski, A.P., Rowiński, P.M. I Napiórkowski, J.J. (2010) Uncertainty study of data-based models of pollutant transport in rivers. Proceedings of River Flow 2010 Conference, Braunschweig, Germany, 8-10 September 2010
• Piotrowski, A.P., Rowinski, P.M. i Napiorkowski, J.J. (2009). Estimation of parameters of models of pollutant transport in rivers depending on data availability. 33rd IAHR Congress: Water Engineering for a Sustainable Environment, Vancouver, 1179-1186.
• Napiórkowski, J.J., Piotrowski, A., Rowiński, P.M. and Wallis, S.G. (2008) Prediction of the fate of pollutants in rivers by means of nonlinear Volterra series, River Flow 2008: Proceedings of the International Conference on Fluvial Hydraulics, Çeşme-İzmir, Turkey, 3-5 September 2008, 2469-2476.
• Kiczko, A., Piotrowski, A., Napiórkowski, J.J. and Romanowicz, R.J. (2008) Combined reservoir management and flow routing modelling: Upper Narew case study, River Flow 2008: Proceedings of the International Conference on Fluvial Hydraulics, Çeşme-İzmir, Turkey, 3-5 September 2008, 1921-1928.
• Rowinski, P.M., Guymer, I., Bielonko, A., Napiorkowski, J.J., Pearson, J., Piotrowski, A. (2007) Large scale tracer study of miting in a natura lowland river w: Proceedings of 32nd IAHR Congress, Venice.
• Wallis, S.G., Piotrowski, A., Rowiński, P.M., Napiorkowski, J.J. (2007) Prediction of dispersion coefficients In a small stream Rusing artificial neural networks w: Proceedings of 32nd IAHR Congress, Venice.
• Piotrowski, A., Rowiński, P.M., Napiórkowski, J.J. (2006) Assessment of longitudinal dispersion coefficient by means of different neural networks w: Proceedings of the 7th International Conference on Hydroinformatics 2006, Nice.
• Napiórkowski, J.J., Piotrowski, A. (2005) Artificial neural networks as an alternative to the Volterra series in rainfall-runoff modelling, Acta Geophysica Polonica, 53(4), 459-472.
• Piotrowski, A., Napiórkowski, J.J. (2005) Dispersion coefficient assessment by means of different neural networks w: Materiały VIII Krajowej Konferencji Algorytmy Ewolucyjne I Optymalizacja Globalna, Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa.
• Piotrowski, A., Rowiński, P.M., Napiórkowski, J.J. (2004) River flow forecast by selected black box models w: River Flow 2004, wydane przez M. Greco, A. Carravetta i R. D. Morte, Leiden.
• Piotrowski, A. (2003) Porównanie prognoz przepływów rzecznych otrzymanych z modeli przestrzeni fazowej i sieci neuronowych w: Współczesne Problemy Hydrauliki Wód Śródlądowych, Materiały XXIII Ogólnopolskiej Szkoły Hydrauliki, Gdańsk.