NDLI: Comprehensive Assessment of Sustainable Utilization of Water Resources Based on RBF Neural Network Evaluation Method

Content Provider	IEEE Xplore Digital Library
Author	Lei Hongjun Liu Xin
Copyright Year	2009
Abstract	Comprehensive assessment of sustainable utilization of regional water resources, including three subsystems: social economy, water resources and eco-environment, is a large complicated and systematic evaluating problem. The selection of the index system for the regional sustainable development of water resources was discussed herein. Furthermore, a neural network methodology for regional water resources evaluation was introduced. The method adopts the Radial Basis Function (RBF) architecture and dynamically penalized rival competitive learning algorithm, which is fast and repetitive, compared with most traditional techniques. Additionally, a new approach to produce training samples, testing samples and examining samples randomly distributed between the critical values was established. The sample used consists of 16 indexes, and by using the proposed methodology the sustainable evaluation of regional water resources could be successfully done. Results showed that from 1994 to 2005, the comprehensive index of water resources sustainable utilization in Zhengzhou city ascended with a fast speed of 0.023 per year, from sustainable development level IV to level III. The calculation with the model showed that, the comprehensive index of water resources sustainable utilization in the future planning years (2010, 2015, and 2020) could be enhanced with a speed of 0.011 per year. A sensitive analysis indicates that it is necessary to increase eco-environmental water consumption rate, industrial output from each cubic meter of water consumption, public greenbelt area per capita, foodstuff output from each cubic meter of water consumption, and to ensure that natural growth rate of population decreases steadily, which are playing an important role in improving sustainable utilization of water resources in Zhengzhou city.
Starting Page	649
Ending Page	653
File Size	371665
Page Count	5
File Format	PDF
ISBN	9780769535593
DOI	10.1109/ICECT.2009.145
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	RBF architecture Regional water resources Artificial neural networks Water conservation Sustainable development Neuron network Neural networks Evaluation index Food industry Cities and towns Sustainable development level Computer networks Resource management Water resources Testing
Content Type	Text
Resource Type	Article

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