Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed

Modeling of hydrological process has been increasingly complicated since we need to take into consideration an increasing number of descriptive variables. In recent years soft computing methods like fuzzy logic and genetic algorithm are being used in modeling complex processes of hydrologic events. The complex non linear behavior of flood and short record of observed data in the region makes the study of flood problematic. This thesis aims to apply soft computing techniques including fuzzy logic, neural network and genetic algorithm on different aspects of flood modeling including hydrological homogeneity and flood prediction in southern Caspian Sea Watersheds. This area with 42400 square kilometers has been affected by severe floods causing damages to human life and properties. A total of 61 hydrometric stations and 31 weather stations with 44 years observed data (1961-2005) are available in the study area. Delineation of homogeneous regions in terms of flood behavior was the initial step of this thesis which was achieved by several methods. The conventional methods of homogeneity i.e. hard clustering (hierarchical and non-hierarchical clustering, K-means) and soft clustering (Fuzzy C-means and Kohonen) were studied and compared by L-moment techniques. Factor analysis using principle component analysis (PCA) with an orthogonal rotation method, varimax factor rotation have resulted in 4 out of 15 parameters namely area, mean elevation, Gravelius factor and shape factor. In conventional hard clustering approach, the number of clusters was determined by hierarchical clustering and two-step cluster analysis; then the sites were allocated to the appropriate cluster by k-means clustering method. In soft clustering approach, Kohonen network was employed to find the number of clusters and then the allocation of sites to the appropriate cluster was performed by using fuzzy c-means method. As a conclusion of regionalization, 38, 13 and 10 catchments were allocated to 3 specified regions. Assessment of homogeneity in this region was achieved and approved by three proposed heterogeneity measures i.e. HLcv, HLck, HLcs with 1.94, 1.13 and 0.71, respectively. In order to fully investigate the homogeneity (h) of catchments and overcome incompatibility that may happen on boundaries of cluster groups, a new method was used which utilizes physical and climatic parameters as well as flood seasonality and geographical location. This approach was based on fuzzy expert system (FES) using Fuzzy Toolbox of MATLAB software. Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. Results obtained by this method were compared to the conventional methods. Since 3 L-moment criteria obtained by this method were significantly lower than previous methods it can be concluded that FES has better performance. After defining homogeneous region in the study area, flood models were developed. A total of 24 sites which were eligible in terms of adequate rainfall and runoff observed data were selected in this region. This area contains 604 pairs of observed data which was grouped into 60%, 20% and 20% for training, validation and testing, respectively. The most popular network in hydrology i.e. Multilayer Feedforward Back Propagation (MLFFBP) was used. Among the available learning algorithms in the Neural Network Toolbox of MATLAB, three algorithms, gradient descent back propagation (TRAINGD), gradient descent with adaptive learning rule back propagation (TRAINGDA) and the Levenberg-Marquardt (TRAINLM) were studied. Three algorithms of Linear (PURELIN), hyperbolic tangent sigmoid (TANSIG) and logistic sigmoid (LOGSIG) activation functions were selected for output layer. The hidden layer includes 1 layer with different neurons. Based on the mentioned criteria several scenarios were defined and compared which resulted to a structure of 8-10-1 with the Levenberg-Marquardt (LM) as the training algorithm and logistic sigmoid function in the output layer. This study found that FES technique is a powerful solution to make pooling groups as a promising approach that satisfies the high homogeneity of the catchments. The application of FES optimized by GA on regionalization creates opportunities for further researches which utilizes different types of optimization like Ant Colony Optimization (ACO), ANN’s, Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA).

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