Chapter 2
Application of a Machine Learning
Technique for Developing Short-Term
Flood and Drought Forecasting Models
in Tropical Mountainous Catchments
Paul Muñoz, Johanna Orellana-Alvear, and Rolando Célleri
Abstract Floods and droughts are among the most common natural hazards worldwide. They produce major impacts on society, economy, and ecosystems. Even worst,
the frequency and severity of hydrological extremes are expected to increase with
climate change and land-use alteration. As a countermeasure, during last decades,
implementation of flood and drought forecasting models have globally become an
emerging field of research for water management and risk assessment. In mountainous areas, hydrological extremes forecasting is unfortunately more challenging
considering that information other than precipitation and runoff is not commonly
available due to budget constraints, remoteness of the study areas and extreme
spatio-temporal variability of additional driving forces. This is especially true for
the tropical Andes in South America, which is the longest and widest cool region in
the tropics. Recent advances in computational science coupled with long-term data
availability have boosted Machine Learning (ML) applications. Among the variety
of ML techniques, there is a potential to use the Random Forest (RF) algorithm due
to its simplicity, robustness and capacity to deal with complex data structures. We
used a step-wise methodology to developed short-term flood and drought forecasting
models for several lead times (4, 8, 12 and 24 h) for two catchment representative of
the Ecuadorian Andes. We found that derived models can reach maximum validation performances (Nash–Sutcliffe efficiency, NSE) from 0.860 (4-h) to 0.545 (24-h)
P. Muñoz (B) · J. Orellana-Alvear · R. Célleri
Departamento de Recursos Hídricos Y Ciencias Ambientales, Universidad de Cuenca, 010150
Cuenca, Ecuador
e-mail: paul.munozp@ucuenca.edu.ec
J. Orellana-Alvear
e-mail: johanna.orellana@ucuenca.ed.ec
R. Célleri
e-mail: rolando.celleri@ucuenca.edu.ec
J. Orellana-Alvear
Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg,
35032 Marburg, Germany
R. Célleri
Facultad de Ingeniería, Universidad de Cuenca, 010150 Cuenca, Ecuador
© Springer Nature Switzerland AG 2021
R. Djalante et al. (eds.), Integrated Research on Disaster Risks, Disaster Risk Reduction,
https://doi.org/10.1007/978-3-030-55563-4_2
11
Application of a Machine Learning
Technique for Developing Short-Term
Flood and Drought Forecasting Models
in Tropical Mountainous Catchments
Paul Muñoz, Johanna Orellana-Alvear, and Rolando Célleri
Abstract Floods and droughts are among the most common natural hazards worldwide. They produce major impacts on society, economy, and ecosystems. Even worst,
the frequency and severity of hydrological extremes are expected to increase with
climate change and land-use alteration. As a countermeasure, during last decades,
implementation of flood and drought forecasting models have globally become an
emerging field of research for water management and risk assessment. In mountainous areas, hydrological extremes forecasting is unfortunately more challenging
considering that information other than precipitation and runoff is not commonly
available due to budget constraints, remoteness of the study areas and extreme
spatio-temporal variability of additional driving forces. This is especially true for
the tropical Andes in South America, which is the longest and widest cool region in
the tropics. Recent advances in computational science coupled with long-term data
availability have boosted Machine Learning (ML) applications. Among the variety
of ML techniques, there is a potential to use the Random Forest (RF) algorithm due
to its simplicity, robustness and capacity to deal with complex data structures. We
used a step-wise methodology to developed short-term flood and drought forecasting
models for several lead times (4, 8, 12 and 24 h) for two catchment representative of
the Ecuadorian Andes. We found that derived models can reach maximum validation performances (Nash–Sutcliffe efficiency, NSE) from 0.860 (4-h) to 0.545 (24-h)
P. Muñoz (B) · J. Orellana-Alvear · R. Célleri
Departamento de Recursos Hídricos Y Ciencias Ambientales, Universidad de Cuenca, 010150
Cuenca, Ecuador
e-mail: paul.munozp@ucuenca.edu.ec
J. Orellana-Alvear
e-mail: johanna.orellana@ucuenca.ed.ec
R. Célleri
e-mail: rolando.celleri@ucuenca.edu.ec
J. Orellana-Alvear
Laboratory for Climatology and Remote Sensing, Faculty of Geography, University of Marburg,
35032 Marburg, Germany
R. Célleri
Facultad de Ingeniería, Universidad de Cuenca, 010150 Cuenca, Ecuador
© Springer Nature Switzerland AG 2021
R. Djalante et al. (eds.), Integrated Research on Disaster Risks, Disaster Risk Reduction,
https://doi.org/10.1007/978-3-030-55563-4_2
11