Watershed models are parameter driven. Therefore, getting a better model performance we need to optimize the parameters. A sensitivity test could help identifying the set of parameter most sensitive parameter for specific watershed. A cleaver idea would be to list down all the parameter in a text file and tune individual parameters and rerun the model each time to see the performance.
Manuel calibration with MATLAB
After building a SWAT model all the input files will be stored in the txtinout folder which should be located in the scenario folder. [Considering discharge and water quality modeling]
Step 1: Copy the input files from txtinout folder to a folder name sensin or we can rename the txtinout folder with the name sensin.
Step 2: make an observe discharge file with the sub basin number [as example “obs_daily7.csv] in put in the folder name user input.
Step 3: Check the ifinfo.txt file located in the user_inputs folder and tunes the specifications with your watershed information. and finally run 'mcalib.m' file.
The good thing is all the parameters are listed in par_ file where we can change parameter and rerun the model until a certain level of accuracy is achieved.
[Applying Genetic algorithm]
Manuel calibration is often time consuming and setting a range of parameter needs a lot of iteration. In that case we can select a set of parameter and the computer does the iterations. There are various optimization algorithms are now available. Here I am going to discuss about AMALGAM. In this video present contribution, the a Multi algorithm genetically adaptive multiobjective (AMALAGM) approach was applied [J.A. Vrugt and B.A. Robinson, 2007]. AMALGAM is a multi-objective optimization method that searches efficiently for the Pareto-optimal front by applying four alternative multiobjective algorithms based on evolution theory.
AMALGAM comprises four different genetic algorithms, they are:
(i) non dominated sorted genetic algorithm-II (NSGA-II) [Deb et al., 2002], (ii) particle swarm optimization (PSO) [Kennedy and Eberhart, 1995], (iii) adaptive Metropolis search (AMS) [Haario et al., 2001], and (iv) differential evolution (DE) [Storn and Price, 1997].By crossing information between algorithms and assigning them more or less importance according to their past performance in the optimization run, the AMALGM approach is able to maintain efficiency and outperform the multi objecive algorithms it employs [J.A. Vrugt 118 and B.A. Robinson, 2007].
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan (2002), A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
Haario, H., E. Saksman, and J. Tamminen (2001), An adaptive Metropolis algorithm, Bernoulli, 470 7(2), 223-242.
Kennedy, J., and R. Eberhart (1995), Particle swarm optimization, IEEE International Conference on Neural Networks - Conference Proceedings, 4, 1942-1948.
Vrugt, J. A., and B. A. Robinson (2007), Improved evolutionary optimization from genetically adaptive multimethod search, Proceedings of the National Academy of Sciences, 104(3), 708.