Desain pembelajaran

Seri pemrograman komputer di bidang sumber daya air dan hidroinformatika menggunakan Python dan R

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1. Dasar-dasar Python dan R (Part 1)

2. Dasar-dasar Python dan R (Part 2)

3. Data hidrologi dan akuisisi data

1. Tipe data hidrologi
2. Download data hujan
3. Dataframes dan membaca data hujan tabuler.
4. Summarizing data
5. Export dataframe

4. Analisis hidrologi

1. Manipulasi dataframe
2. Filtering and grouping temporal series
3. Historical analysis of precipitation
4. Precipitation and streamflow data analysis and visualization
5. Analysis of streamflow and rainfall relationship
6. Precipitation based calculations

5. Data spasial (pengantar GIS)

1. Read satellite rainfall data
2. Spatial analysis and visualization
3. Mapping

6. Data spatio-temporal (3-dimension array)

1. Reading multilayer data
2. Filtering and grouping temporal series
3. Satellite / remote sensing data analysis
4. Spatiotemporal operation

7. Statistics

1. Regression
2. Statistical distribution
3. Return period (frequency analysis)
4. Trend, stationarity, periodicity
5. Correlation analysis
6. Ketidakpastian (uncertainty)

8. Advance Plotting

Visualisasi data menggunakan berbagai library seperti Matplotlib, Seaborn.

1. Barchart
2. Boxplot
3. Error bar
4. Q-Q plot
5. Double axis precipitation and streamflow visualization
6. More advanced plotting

9. Integrating user interface

1. Menyiapkan engine
2. Graphical User Interface

10. Special topic: Filling missing precipitation data

Implementation of different methods in Python to fill gaps in precipitation or other water related variable.

11. Special topic: Interpolation of Precipitation Data with Python and Matplotlib

1. Exploring precipitation data with Python.
2. Define stations location with Folium.
3. Plot precipitation values with following methods: Linear, Cubic and Nearest
4. Creation of a plot function for interpolation precipitation data.

12. Special topic: Climate variable exploration from multiple climate stations

Advanced example of exploratory data analysis of numeruous climate variables and stations with Python. Stations location and data was analyzed using various tools and visualized.

13. Special topic: Machine learning in Python for water resources

Algorithms of machine learning in Python for predictive data analysis can be applied to any field of water resources-related analysis. Developing some applied cases of machine learning prediction with the Scikit Learn.

14. Special topic: Future projection data analysis based on CMIP6

Analysis of CMIP data such as as precipitation and temperature by 2100. Data exploration and metric generation.

1. Exploring climate data with Python and Pandas.
2. Temporal distribution of climate data
3. Analyzing minimum and maximum temperature data.
4. Estimating trends regarding climate change.
5. Checking the variability for decades.