Monday, July 17, 2017

Modeling flood damages

While working on a investment planning model to combat damages resulting from flooding, I received the results from a rainfall model that calculates damages as a result of excessive rain and flooding. The picture the engineers produced looks like:


This picture has the scenario on the x-axis (sorted by damage) and the damages on the y-axis. This picture is very much like a load-duration curve in power generation.

For a more “statistical” picture we can use standard histogram (after binning the data):


Gamma distribution

We can use standard techniques to fit a distribution. When considering a Gamma distribution (1), a simple approach is the method of moments. The mean and the variance of the Gamma distribution with parameters \(k\) and \(\theta\) are given by:

\[\begin{align} &E[X] = k \cdot \theta\\ & Var[X] = k \cdot \theta^2 \end{align} \]

Using sample mean \(\mu\) and standard deviation \(\sigma\), we can solve:

\[\begin{align} & k \cdot \theta = \mu\\ & \sqrt{k} \cdot \theta = \sigma \end{align} \]

This can be easily solved numerically and it actually seems to work:


Weibull distribution

An alternative distribution that is sometimes suggested is the Weibull distribution (2).  The method of moments estimator for the Weibull distribution with parameters \(\lambda\) and \(k\) can be found by solving the system:

\[\begin{align} & \lambda \Gamma(1+1/k) = \mu\\ &\lambda \sqrt{\Gamma(1+2/k)+\left(\Gamma(1+1/k)\right)^2} = \sigma \end{align}\]
I was unable to get a solution from this: solvers failed miserably on this.

An alternative approach would be to use an MLE (Maximum Likelihood Estimation) technique. This yields a system of equations:

\[\begin{align} & \lambda^k = \frac{1}{n}\sum_{i=1}^n x_i^k\\ &k^{-1} = \frac{\sum_{i=1}^n x_i^k \ln x_i}{\sum_{i=1}^n x_i^k} – \frac{1}{n}\sum_{i=1}^n \ln x_i \end{align}\]
Note that we can solve the second equation first to solve for \(k\) and then calculate \(\lambda\) using the first equation. A solver like CONOPT will do this automatically by recognizing the triangular structure of this system. Note that our data set contains many \(x_i=0\). These are replaced by \(x_i=0.001\) so we can take the logarithm.

This gives is a very similar picture:


  1. Gamma distribution,
  2. Weibull distribution,

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