Yurii Malna: Deep Learning for Flood Forecasting AI & BigData Online Day 2021 Website - http://aiconf.com.ua Youtube - https://www.youtube.com/startuplviv FB - https://www.facebook.com/aiconf

1. Deep Learning For
Flood Forecasting
Yurii Malna
Data Scientist

2. Slide
#2
Agenda
• The Importance of Flood Forecasting
• Hydrology vs. Machine Learning
• Case Study
• When Hydrologist Meets ML
• LSTM for Rainfall-Runoff Modelling
• LSTM Model Interpretability
• Flood Forecasting and InsurTech
• Q&A

3. Slide
#3
The Importance of Flood Forecasting
• Loss of lives and property
• Loss of livelihoods
• Depleted purchasing and production power
• Mass migration
$75
Billion
$170
Billion
past 2 decades
next 2 decades

4. Slide
#4
Hydrology vs. Machine Learning
HBV Hydrology Model

5. Slide
#5
Case Study
• 24 Snow-influenced measuring stations
in USA
• Daily-scale meteorological and
hydrological parameters (from
1980/01/01 to 2014/12/31)
• Baseline: National Water Model (NWM)
developed by US NOAA
• Raw features used:
 Precipitation, mm/day
 Solar Radiation W/m2
 Min. Temperature, C
 Max. Temperature, C
 Vapor Pressure, Pa
• Target Variable: Streamflow [72h],

6. Slide
#6
When Hydrologist Meets ML

7. Slide
#7
When Hydrologist Meets ML

8. Slide
#8
LSTM for Rainfall-Runoff Modeling
• Perfect way to dive deep into making a good quality forecast
• From a Hydrological perspective, LSTM architecture fits well into mimicking
the Hydrological Response of a Catchment
• Remembering long sequences is crucial for robust Rainfall-Runoff model
https://becominghuman.ai/long-short-term-memory-part-1-3caca9889bbc

9. Slide
#9
LSTM for Rainfall-Runoff Modeling
• LSTM Input tensor: (batch_size x sequence_length x n_features )
• Cross-Validation used: Rolling 10-k split CV
• LSTM architecture: Simple 2-layer stacked LSTM network
Input LSTM LSTM Dense Output
• LSTM activation function: Leaky-ReLU
• LR policy: LR Range Test + Cyclic Triangular LR policy with reset after 30
epochs
• Dataset pre-processing: Standardization (remove mean and scale to unit
variance)

10. Slide
#10
LSTM for Rainfall-Runoff Modeling
Predicted vs. Observed Flow
Predicted vs. Observed Flow, 2011 Flood Event

11. Slide
#11
LSTM for Rainfall-Runoff Modeling
NSE(LSTM)=0.869
RMSE(LSTM)=154.216
NSE(NWM)=0.719
RMSE(NWM)=277.633
Predicted vs. Observed Flow Predicted vs. Observed Flow, 2011 Flood Event

12. Slide
#12
LSTM Model Interpretability

13. Slide
#13
LSTM Model Interpretability

14. Slide
#14
Flood Forecasting and InsureTech
• Most flood impacts are avoidable with the right early
warning systems
• Policyholders can implement their flood action
• Increase Awareness  Decrease Vulnerability  Reduce
Losses
Having an accurate Flood Forecast is
beneficial for Insurance Companies
https://previsico.com

15. Slide
#15
Flood Forecasting and InsureTech
Fargo, North Dakota, USA
2011 Flood in Fargo, North Dakota, USA 2011 Modeled Flood (input: NWM)
2011 Modeled Flood: GT vs. NWM 2D
2011 Modeled Flood (input:
LSTM)
2011 Modeled Flood: GT vs. LSTM 2D

16. Q&A