River Flow Forecasting Using Support Vector Machines

Over the past few months I and a colleague (Brian Wallace) have been working on a river flow forecasting paper. A draft version is available @ River Flow Paper.

The goal of our work was to beat the current forecast methods used by the Department of Water Resources for the April through July American River flow. The Department of Water Resources uses an aggregation of human judgement and linear regression equations for generating their forecasts. Given their methods they are surprisingly hard to beat!

We spent a few months trying different Machine Learning methods with little success. Many of the methods we tried resulted in forecasts that were significantly worse than the current forecasts, a few methods such as a properly trained neural network gave forecasts that were comparable to the current forecasts. Finally, I decided to use a Support Vector Machine (SVM) for producing forecasts, after testing a large combination of parameters the forecasts started being significantly better than the current ones.

The data we used for generating forecasts is available online @ https://github.com/bjwbell/California-Water-Runoff-Forecasting. The takeaway message is that we improved the forecast relative error from ~65% to ~48%. The below table shows the forecasts for the last 10 years.

SVM Forecasts 2001-2010
Year Actual (AcreFt)   Predicted (AcreFt)   |Error| (AcreFt)  
2001    552,626 689,472 136,846
2002    973,817 1,028,681 54,864
2003    1,354,434 459,476 894,957
2004    632,159 713,440 81,281
2005    2,003,878 1,844,360 159,517
2006    2,622,387 2,315,193 307,193
2007    522,651 293,256 229,394
2008    674,287 800,080 125,793
2009    1,068,327 1,253,523 185,196
2010    1,486,780 1,023,649 463,130
Mean 1,189,135 1,042,113 263,817
Root mean squared error 355,856
Relative absolute error 48.65%
Root relative squared error 54.14%

The forecasts currently used by the Department of Water Resources produced relative errors of 63.82% and root relative squared errors of 69.15%. Using modern methods for SVM’s gave us an increase in relative accuracy of over 15%! This was a fantastic result and shows the large payoff in keeping up with the state of art for something as ordinary as river flow forecasting.


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