Dr.-Ing. Leonardo Francisco Arias Rodriguez


Foto von Leonardo Francisco Arias Rodriguez

Technische Universität München

Lehrstuhl für Hydrologie und Flussgebietsmanagement (Prof. Disse)

Postadresse

Postal:
Arcisstr. 21
80333 München

Dienstort

Lehrstuhl für Hydrologie und Flussgebietsmanagement (Prof. Disse)

Work:
Augustenstr. 44_46(2903)/II
80333 München

Ausbildung

  • 2022 | Promotion (Dr.-Ing.) am Lehrstuhl für Hydrologie und Flussgebietsmanagement, TU München | Dissertation Thema: "Monitoring Water Quality of Inland Waters by Remote Sensing and Machine Learning: From Local to Global Applications" | München 
  • Seit 2018 | Doktorand am Lehrstuhl für Hydrologie und Flussgebietsmanagement, TU München | München
  • 2015 – 2018 | M.Sc. in Umweltingenieurwesen, TU München | München
  • 2013 – 2014 | Spezialisierungsgrad in Sanitäringenieurwesen, Nationale Autonome Universität von Mexiko, UNAM Mexiko-Stadt
  • 2010 | Internationales Auslandssemester, Universidad Politècnica de Catalunya, UPC Barcelona
  • 2006 – 2011 B.Sc. Bauingenieur (Dipl.-Eng), Autonome Universität von Aguascalientes, UAA Aguascalientes

Beruflicher Werdegang

  • Seit 2019 | Wissenschaftlicher Assistent am Lehrstuhl für Hydrologie und Flussgebietsmanagement, TU Munich Project: "Water Quality of Inland Waters by Remote Sensing and Machine Learning Approaches"
  • 2014 | Lehrassistent an der Abteilung für Sanitär- und Umwelttechnik, Nationale Autonome Universität von Mexiko, UNAM | Fach: Water Supply Network Design
  • 2013 – 2014 | Umweltingenieurwesen Beratung und Lehrer bei Proyecto Tierra S.C., Mexiko-Stadt
  • 2011 – 2013 | Bauingenieur bei der Stadtverwaltung Jesús María, Aguascalientes
  • 2010 | Bauingenieur bei SIDOC S.A. de C.V.

Publikationen

Journal Papers

  • Arias-Rodriguez, L.F.; Tuzun, U.F.; Duan, Z.; Huang, J.; Tuo, Y.; Disse, M., (2022) Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 using Cloud-Computed Machine Learning, (Submitted for peer review)
  • Adla, S., Bruckmaier, F., Arias-Rodriguez, L.F., Tripathi, S., Disse, M., and Pande, S., (2022), Impact of calibrating a low-cost capacitance-based soil moisture sensor on FAO AquaCrop model performance, (Under review)
  • Tran YB, Arias-Rodriguez LF and Huang J (2022) Predicting high-frequency nutrient dynamics in the Danube River with surrogate models using sensors and Random Forest. Front. Water 4:894548. https://doi.org/10.3389/frwa.2022.894548
  • Hoyek, A.; Arias-Rodriguez, L.F.; Perosa, F. (2022) Holistic Approach for Estimating Water Quality Ecosystem Services of Danube Floodplains: Field Measures, Remote Sensing, and Machine Learning. Hydrobiology1, 211-231. https://doi.org/10.3390/hydrobiology1020016
  • F. Hofmeister, L.F. Arias-Rodriguez, V. Premier, C. Marin, C. Notarnicola, M. Disse, G. Chiogna,  (2022), Intercomparison of Sentinel-2 and modelled snow cover maps in a high-elevation Alpine catchment, Journal of Hydrology X, doi: https://doi.org/10.1016/j.hydroa.2022.100123
  • Arias-Rodriguez, L.F.; Duan, Z.; Díaz-Torres, J.d.J.; Basilio Hazas, M.; Huang, J.; Kumar, B.U.; Tuo, Y.; Disse, M. (2021) Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors21, 4118. https://doi.org/10.3390/s21124118
  • Perosa, F.; Gelhaus, M.; Zwirglmaier, V.; Arias-Rodriguez, L.F.; Zingraff-Hamed, A.; Cyffka, B.; Disse, M. (2021) Integrated Valuation of Nature-Based Solutions Using TESSA: Three Floodplain Restoration Studies in the Danube Catchment. Sustainability13, 1482. https://doi.org/10.3390/su13031482
  • Arias-Rodriguez, L.F.; Duan, Z.; Sepúlveda, R.; Martinez-Martinez, S.I.; Disse, M. (2020) Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches. Remote Sens.12, 1586. https://doi.org/10.3390/rs12101586

Conference Papers

  • Adla, S., Bruckmaier, F., Arias-Rodriguez, L. F., Tripathi, S., Disse, M., and Pande, S.: Analysing the impact of calibrating a low-cost soil moisture sensor on FAO Aquacrop model performance., EGU General Assembly (2022), Vienna, Austria, 23-27 May 2022, EGU22-11810, https://doi.org/10.5194/egusphere-egu22-11810 
  • Huang, J., Tran, B. Y., and Arias-Rodriguez, L. F.: Predicting high-frequency nutrient dynamics in the Danube River from surrogates with sensors and machine-learning, EGU General Assembly 2022, Vienna, Austria, 23-27 May (2022), EGU22-12914, https://doi.org/10.5194/egusphere-egu22-12914 
  • Hofmeister, F., Arias-Rodriguez, L. F., Borga, M., Premier, V., Marin, C., Notarnicola, C., ... & Chiogna, G. (2021). Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration (No. EGU21-16016). Copernicus Meetings.
  • Perosa, F., Gelhaus, M., Zwirglmaier, V., Arias-Rodriguez, L. F., Zingraff-Hamed, A., Cyffka, B., & Disse, M. (2021). Examples of Floodplain Restoration Evaluation Studies in the Danube River Basin. In EGU General Assembly Conference Abstracts (pp. EGU21-7039).
  • LF Arias-Rodriguez, Z Duan, R Sepulveda, SI Martinez-Martinez, M Disse (2019) Evaluation of satellite-based estimation of water quality parameters in Reservoir Valle de Bravo in Mexico. EGU General Assembly Conference Abstracts, 6219.

Eingeladene Vorträge

  • Arias-Rodriguez, L. F. December 2021. Integration of Water Quality Monitoring Systems and Remote Sensing Data. Chair of Hydrology and River Basin Management, Technical University of Munich (Germany).
  • Arias-Rodriguez, L. F. October 2018. Generation of Water Quality Algorithms using Remote Sensing and Field Data. Center of Design and Construction Sciences, Autonomous University of Aguascalientes (Mexico).