Overview

Teaching: 5 min
Exercises: 0 min

Questions

• What are some machine learning applications in water resources research?

• What are some basic examples of libraries to explore?

Objectives

• Learn where you can find machine learning resources

Relevant Reviews of Machine Learning Applications in Water Resources Research

• Shen (2018): A transdisciplinary review of deep learning research and its relevance for water resources scientists, Water Resources Researh.

• Raghavendra and Deka (2014): Support vector machine applications in the field of hydrology: A review, Applied Soft Computing.

• Yaseen et al. (2015): Artificial intelligence based models for stream-flow forecasting: 2000–2015, Journal of Hydrology.

• And many more researches, and you may be contributing to the future ones!

ML Ethics

Articles about bias/politics/etc. in machine learning applications

Computer Vision

1. opencv

Support Vector Machine/Random Forest/etc.

1. scikit-learn
2. vowpal wabbit
3. xgboost

Deep Learning

1. AWS Deep learning AMI
2. MXNet
3. TensorFlow
4. Caffe
5. pyTorch (python Torch)
6. Keras (api for python that plays with TensorFlow, Theano, and others)

ESRI + QGIS

Machine learning capabilities exist in both ESRI & QGIS (support vector machine), however for scaling to large image sets, opensource scripting is nicely adaptable.

Mapbox & Development Seed

(super cool geospatial companies with international offices)

1. Mapbox on github: they have some awesome tutorials ad open source tools to check out
2. DevSeed onn github: Related to Mapbox, they do a lot of GIS Deep Learning
3. robosat
4. training data from OSM

Other Discussion of Learning

Gradient Descent & Cost/Loss Functions

Key Points

• machine learning resources, water applications

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