Portfolio

Academic Projects

  • MSc. Water Resources Engineering Student

    KU Leuven - VUB

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  • Certificate in Machine Learning Cloud

    Universidad Católica Boliviana San Pablo

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  • Bachelor of Science in Civil Engineering

    Universidad Mayor de San Simón

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Master Water Resources Engineering


Katholieke Universiteit Leuven (KU Leuven) - Vrije Universiteit Brussel (VUB)


Main Courses

Earth Observation & Remote Sensing


The main skills developed are:

- Microwave Remote Sensing (Sentinel-1 + SMAP) → hydrology/drought/flood.

Sentinel-1 backscatter analysis (VV, VH, γ⁰) and orbit comparison

Soil moisture analysis using SMAP data (seasonal means, climatology, anomalies).


- Optical Remote Sensing (Sentinel-2 + GEE) → land cover classification.

Unsupervised (K-means) and supervised (Random Forest) land cover classification.

Accuracy assessment using confusion matrix, accuracy, and Kappa.



Technical skills


- Languages: Python.


- Python Libraries: requests, jsonpath_ng, Beautiful Soup, Pandas, Numpy, Scipy, Matplotlib, Seaborn, Scikit-Learn, Tensorflow, Azure Machine Learning.


SDK, pickle


- Version Control Systems: Git and GitHub.


- Development Tools: Visual Studio.



Principal projects


- Implementation of an application with dagster for webscrapping lake information ( Github ).
- Statistical Downscaling of precipitation for climate change scenarios using Machine Learning methods ( Github ).
- Rainfall Prediction in Australia using Azure Machine Learning ( Github ).
- Olympic Games Exploratory Data Analysis ( Github ).
- Water potability precipitation using Machine Learning methods ( Github ).

dagster_deployment
Webscrapping lake information deployment.
Made using Dagster.
dagster_run
Webscrapping lake information run
Made using Dagster.
az-rain-prediction
Rain Prediction.
Made using Azure Machine Learning, Scikit-Learn
az-sd
Statistical Downscaling of precipitation.
Made using Azure Machine Learning, Scikit-Learn
az-eda-2
Statistical Data Analysis, Olympic Games.
Made usingMade using Python, Pandas, and SeaBorn.
az-eda-1
Statistical Data Analysis Normality, Olympic Games.
Made using Python, Scipy and Matplotlib.

Certificate in Machine Learning Cloud


Universidad Católica Boliviana San Pablo


Description

This course provides competencies in using cloud computing resources to perform machine learning tasks that allow machines to learn from data and improve their performance for information analysis, pattern recognition, and process automation.


The main skills developed are:

- Extract, transform, prepare, and load data for applications.


- Data Wrangling and Statistical Data Analysis.


- Machine Learning Cloud in Azure Machine Learning.
- Develop data pipeline orchestrators using Dagster , and Azure Machine Learning.

- Automation of machine learning models and hyperparameter tuning.


- Deploy and use of a cloud model.



Technical skills


- Languages: Python.


- Python Libraries: requests, jsonpath_ng, Beautiful Soup, Pandas, Numpy, Scipy, Matplotlib, Seaborn, Scikit-Learn, Tensorflow, Azure Machine Learning.


SDK, pickle


- Version Control Systems: Git and GitHub.


- Development Tools: Visual Studio.



Principal projects


- Implementation of an application with dagster for webscrapping lake information ( Github ).
- Statistical Downscaling of precipitation for climate change scenarios using Machine Learning methods ( Github ).
- Rainfall Prediction in Australia using Azure Machine Learning ( Github ).
- Olympic Games Exploratory Data Analysis ( Github ).
- Water potability precipitation using Machine Learning methods ( Github ).

dagster_deployment
Webscrapping lake information deployment.
Made using Dagster.
dagster_run
Webscrapping lake information run
Made using Dagster.
az-rain-prediction
Rain Prediction.
Made using Azure Machine Learning, Scikit-Learn
az-sd
Statistical Downscaling of precipitation.
Made using Azure Machine Learning, Scikit-Learn
az-eda-2
Statistical Data Analysis, Olympic Games.
Made usingMade using Python, Pandas, and SeaBorn.
az-eda-1
Statistical Data Analysis Normality, Olympic Games.
Made using Python, Scipy and Matplotlib.

Bachelor Of Science in Civil Engineering

Universidad Mayor de San Simón


Thesis project: Impact of Climate Change on the use of water potential in a pilot basin in Bolivia

Description


The degree project aimed to assess the potential effects of climate change on determining the hydrological level of a reservoir. To achieve this, a case study was conducted on a dam planned for the upper basin of the Piraí River, in Bolivia. The adopted climate change scenarios utilize two representative greenhouse gas concentration trajectories, namely RCP 4.5 and RCP 8.5 in two future periods: 2050s (2036-2065) and 2080s (2070-2099). These are applied using three statistical downscaling methods:


- Quantile Delta Mapping (QDM) ( Cannon, A. 2015 ).
- K-Narest-Neighbor ( Yates, D. & Gangopadhyay, S. 2015 ).
- Delta Change ( Teutschbein, C., & Seibert, J. 2012 ).

To achieve the goals, I created two resource tools:


- dClimate: an application to automate the process of statistical downscaling regional climate models with QDM and Delta Change. ( GitHub ). - Reservoir modeling: a script to perform the reservoir simulation ( GitHub ).

Technical skills


- Hydrology Modeling: HydroBID.


- Languages: Python and R.


- Python Libraries: NumPy, Pandas, Xarray, NetCDF, Matplotlib, Plotly, Tkinter


- Version Control Systems: Git and GitHub.


- Development Tools: Spyder and Rstudio



dagster_deployment
Projected changes in multiyear monthly average precipitation.
Made using dClimate, Matplotlib.
dagster_run
Projected changes in multiyear monthly average temperature.
Made using dClimate, Matplotlib
az-rain-prediction
Changes in indexes used for projected precipitation assessment, using Expert Team on Climate Change Detection and Indices
Made using Python, Matplotlib.
az-sd
Hydrology Modeling duration curves of observed and simulated flow rates.
Made using HydroBID, Matplotlib.
az-eda-2
Projected changes in multiyear monthly average discharge.
Made using HydroBID, Matplotlib.
az-eda-1
Runoff projection envelope considering adoption of results.
Made using HydroBID, Matplotlib.
az-eda-1
Location of Angostura Reservoir (Santa Cruz, Bolivia).
Made using QGIS.
az-eda-1
Reservoir height vs. demand service ratio period 2080s.
Made using Python, and Matplotlib.



Contact


Feel free to contact me for any question. For open source projects, please open an issue or pull request on Github . If you want to follow my work, reach me on Linkedin. Otherwise, send me an email at caldunate.lp@gmail.com .
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