Title :
Evaluation of Water Resources Carrying Capacity in Shandong Province Based on Spatial Cluster
Author :
Wang, Yan ; Cao, Junru ; Meng, Qingmei ; Song, Zhenbai
Author_Institution :
Sch. of Archit. Eng., Shandong Univ. of Technol., Zibo, China
Abstract :
Water resources carrying capacity is a crucial part of regional natural resources carrying capacity, which is a restriction factor in the region tightly short of water resources whether can support harmonious development of population, economy and environment. The major difficulties for Water resources carrying capacity assessment was to determine the weight of each indicator. Illuminated by this, this paper applies spatial cluster analysis to the study of the water resources carrying capacity . Based on the evaluation indicator system of water resources carrying capacity developed in the paper, k-means clustering and neural network model are used to evaluate the water resources carrying capacity in Shandong Province. The case analysis indicates that neural network theory can better evaluate the water resources carrying capacity. Its outcome is more sensible, and it can be used as a scientific guide for strategic decisions regarding sustainable utilization of water resource in Shandong Province.
Keywords :
neural nets; pattern clustering; set theory; statistical analysis; sustainable development; water resources; Shandong Province; evaluation indicator system; k-means clustering; neural network; restriction factor; spatial cluster analysis; water resources carrying capacity; Artificial neural networks; Cities and towns; Clustering algorithms; Economic indicators; Neurons; Training; Water resources; Artificial neural network(ANN); evaluation; k-means; spatial clustering; water resources carrying capacity;
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
DOI :
10.1109/AICI.2010.344
