关键词:
Fairly water distribution
Operational modernization
Aquifer storage and recovery
Hard clustering
Automated system
摘要:
The present study proposed a smart operating system consists of two primary components of i) unsupervised pattern recognition and ii) automated control system. The main goal is shifting the technical orientation of the single-objective automated operating system to a practical one included the sustainable development viewpoints, where the environmental, social, and economic considerations determine the priorities of surface water distribution within an irrigation district. A complementary component consists of the principal component analysis (PCA), and a crisp clustering method is developed to fulfill this objective. The proposed method was implemented in a real test case, Roodasht Irrigation District, in Iran. According to the PCA analysis, six features, encompassing socio-economic, environmental, and technical objectives, were selected as the clustering?s dominant features. The district was regionalized to 6 regions based on the initial results of the clustering. The integration of a couple of the clusters occurred in the post-processing stage, and finally, the test case was grouped into 4 regions with different priorities for receiving the surface water. The priorities were served as penalty values orders in the objective function of the developed automated controller. A comparison of the developed smart system?s obtained results by status quo reveals that moderately water distribution happened in normal and water shortage scenarios, where the Equity index obtained 8.5% and 11.5%, respectively. Also, reliable and adequate water delivered to regions belongs to the forth and first clusters, where 37% and 38% of the region supplied more than 80% of their demands under the two scenarios, respectively. It is worth noting that the mentioned achievement created in the forth and first clusters, with the minimum groundwater overexploitation, the maximum dependency of farmers on agricultural activities incomes, and the seasonal worker?s highest rate of unemployment. The d