Juan Carlos Chacon Hurtado

PhD fellow


Born in 1986, in Cali, Colombia, Juan Carlos is a Civil Engineer (Pontificia Universidad Javeriana Cali, Colombia) with MSc in Water Science and Engineering, specialisation in Hydroinformatics (UNESCO-IHE, Netherlands). During his professional life, he has worked in areas of hydrological modelling for operational flow forecasting systems, water loss control in urban distribution systems and slope stability analysis and design. His links with academia are strong, devoting most of his time in research and applied research consultancy.

His research interests are diverse, among which are:

  • Hydrological modelling
  • Data-driven modelling
  • Mathematical and statistical modelling
  • Optimisation
  • Hydraulic and hydrological monitoring
  • Data assimilation
  • Water loss control in urban distribution systems

Additionally to this, it is of great interest the development of open source modelling tools, such as the HIPy initiative.


Dynamic multi-objective optimisation of dynamic heterogeneous networks of physical and social sensors


Chacon-Hurtado, J. Alfonso, L. Solomatine, D. 2014. Precipitation sensor network optimal design using time-space varying correlation structure . 11th International confence in Hydroinformatics, HIC 2014. New York City, USA.

Chacon-Hurtado, J. Alfonso, L. Solomatine, D. 2014. Dynamic correlation structure for precipitation interpolation. European Geosciences Union. Vienna, Austria.

Chacon-Hurtado, J. Xu, Y. Alfonso, L. Solomatine, D. 2014. Maximum Falsifiability approach for model calibration. European Geosciences Union. Vienna, Austria.

Chacon-Hurtado, J. Alfonso, L. Solomatine, D. 2014. Comparison of Machine Learning approaches for precipitation data infilling. European Geosciences Union. Vienna, Austria.

Garzon, F. Chacon-Hurtado, J. 2012. Interdependence account for physical loss control alternatives to estimate an economic level of leakage. IWA Water Loss Conference 2012. Manila, Philippines.

Garzon, F. Chacon-Hurtado, J. 2010. What is the optimal DMA size in the Latin-American context?. IWA Water Loss Conference 2010. Sao-Paulo, Brazil.

Chacon-Hurtado, J. Garzon, F. Montana, D. 2009. Optimizacion de la red de pluviometros de la ciudad de Cali, Colombia, por metodos geoestadisticos (Optimization of the rain gauge network in Cali, Colombia using geostatistical methods. In Spanish). Seminar: Estrategias para enfrentar el cambio climatico (Strategies to face climate change), Agua 2009. Cali, Colombia.

Caicedo, M. Escobar, J. Garzon, F. Chacon-Hurtado, J. 2009. Modelos de decision para la renovacion eficiente de redes de acueducto. Caso de estudio: Santiago de Cali, Colombia. (Decision models for the efficient renewal in water supply network. Case Study: Cali, Colombia. ) Seminar: Un nuevo paradigma en la gestion integral del agua en zonas urbanas (A new paradigm in urban water management), Agua 2009. Cali, Colombia.

Taborda, P. Gongora, M. Garzon, F. Chacon-Hurtado, J. 2009. Uso residencial del agua en la ciudad de Santiago de Cali, Colombia (Residential water use in the city of Santiago de Cali, Colombia.). Seminar: Uso eficiente del agua (Efficient water use), Agua 2009. Cali, Colombia.

Garzon, F. Salinas, M. Taborda, P.  Chacon-Hurtado, J. 2009. The Uncertainty On Apparent Losses Setting Targets In Latin America. Proceedings of the 5th IWA Water loss reduction specialist conference. Cape Town, South Africa.

Chacon-Hurtado, J. Garzon, F. 2007. Analisis cuantitativo de riesgo de fenomenos de remocion en masa (Quantitative analysis of landslide phenomena risk, in spanish). XVIII National congress and VII International congress of Civil Engineering students ANEIC. Armenia, Colombia.

Other information


Current theories and methods for designing monitoring networks aim to place traditional, fixed sensors to accurately infer spatial and temporal state of water systems and forecasts. Citizen Observatory of Water, however, needs to couple data from diverse sources forming a network of fixed and dynamic sensors providing physical and social data, identify the best spatial and temporal data needs, allowing dynamic sensors, such as those carried along by citizens, to complement the information coming from traditional fixed sensors and remote sensing tools. The main objective of this study will be to develop and test the mathematical and algorithmic framework for dynamic network optimization, able to identify the optimal sensor locations, variables to measure, time coverage, and reliability range in real-time providing the best possible information content, e.g., for a flood or water quality forecast. Additional tasks relate to the fact that physical and social sensors may be providing conflicting information, and this would need the selection of dedicated dynamic social sensors (humans activating the carried physical sensors, or sending verbal information) to be requested to visit particular locations of interest to resolve a conflict. Multi-objective optimization methods to be tested and refined will include methods developed in the area of computational intelligence (evolutionary and adaptive random search algorithms), made however more robust due to uncertain data, and more efficient due to the use of computationally intensive models. In computing, use of parallelization using clusters and cloud computing is foreseen.