Exploring the Effective Evaluation Indices of Self-Organizing Map for Clustering Regional Flood Inundation Map

• Main Authors: Wu-Han Wang, 王梧翰
• Other Authors: Li-Chiu Chang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/sdu4s7

碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 106 === Today, Artificial Intelligence is one of popular issues with many research topics and practical applications. The relative AI issues on the study of water resource management or flood forecast have become one of important topics. The purpose of this study is to propose the methodology to automatically build the Self-organizing maps (SOM) on clustering the flood spatial distribution. There are three major problems on building the SOM model; first one is the topological error, that is, any two neurons flip each other weights that makes the order of the topological map; second one is to the selection of the number of epochs. The training algorithm of SOM has two phases, ordering phase and convergent phase. Hence, these two phases have the different number of epochs and the number of epochs can influence the convergence; third one is to decide the optimal size. This study proposes two training strategies of the SOM models and takes Luermen Creek and Yenshui Creek located in Tainan, and Kemaman River located in Terengganu of Malaysia to investigate the convergence of the SOM models. The first strategy, called plan1, is to train the network in the ordering phase until the weights of the neurons have no obvious change, then transfer to the convergent phase and continue training the neurons until the weights have no obvious change. The second strategy, called plan2, is to rain the network in the ordering phase until the coverage rate of weights reaches 50%, then transfer to the convergent phase and continue training the same as the convergent phase of plan1. We use the flood simulation data of these three areas as the training data to build their own models. Through the different training strategy of plan1 and plan2, we can explore the influences of the ordering and convergent phases on building the SOM models. Through coverage rate, flip detector and five indices to compare the clustering results of the SOM clustering results. The coverage rate is defined as the difference of the cumulative distribution rates between maximum and minimum weights (neurons). The flip detector can check whether any two or more neurons flip each other weights or not and determine topological order correct or not. The clustering results of these three cases show that the number of epochs can influence the coverage rate and effectively improve the clustering quality. The larger number of epochs can get the larger coverage rate. The results show that plan2 can get convergent clustering results while plan1 occurs flip in Luermen Creek and Kemaman River. Hence plan2 is more suitable than plan1 for applying the SOM model on clustering the flood spatial distribution. Moreover, for comparison of the different size of the SOM models, the results demonstrate that the coverage rates of 3×3 model are smaller than those of 4×4 and 5×5 models, about 5%-10% less. That means 3×3 model cannot describe the characteristics of data as well as 4×4 and 5×5 models. The coverage rates of 4×4 and 5×5 models are almost the same, so the small models should be enough neurons to describe the data, that is, 4×4 is an appropriate size than other models. Hence, for choosing the size of topology map, the coverage rate is the great index to decide the optimal size.