Artificial Intelligence for City Flood Control System
Date Issued
2009
Date
2009
Author(s)
Chang, Kai-Yao
Abstract
Drainage systems play an important role in transporting storm runoff and reducing flood risk in urban areas. Stormy water, discharged by underground drainage systems, is hard to control, especially in highly urbanized areas, where concentration time is shorten and runoff coefficients are increased. This study aims to construct water-level prediction models in urban drainage systems and real-time operational guidelines for flood control pumping stations by using artificial intelligent techniques (AI). The AI techniques could effectively solve highly non-linear control problems and robustly tune the complicated conversion of human intelligence to logical operating system.his study first applies back-propagation neural networks (BPNN) to predict water-level in the urban drainage systems of Taipei city. The results show that BPNN could satisfyingly predict the water level with high accuracy. The model provides much longer responding time for urban flood management. The application also indicates that input data with shorter time interval has higher accuracy, which meets the need of pumping operation.he real-time operation guidelines for pumping stations in urban areas are future investigated by using counterpropagation fuzzy-neural network (CFNN) and adaptive network-based fuzzy inference system (ANFIS). The results demonstrate that CFNN and ANFIS are both capable of forming reliable guidelines by using the information of precipitations, fore-bay water levels, gate operation and number of pumping station. It also indicates that ANFIS, comparing to CFNN, has better learning algorithm, which requires less rules to meet accuracy pumping operation needs. The real-time operation guidelines formed by ANFIS are recommended to managers for promoting operation efficiency and reliability.
Subjects
Water-level prediction
Pumping operation
Back-propagation neural networks(BPNN)
Counterpropatagation fuzzy neural network(CFNN)
Adaptive network-based fuzzy inference system(ANFIS)
Type
thesis
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