Evaluation and Clustering Maps of Groundwater Wells in the Red Beds of Chengdu, Sichuan, China

Abstract

Abstract:Since the start of the 21st century, groundwater wells have been placed in red beds to solve the problem of scarce water resources in Southwest China and have rapidly expanded to other areas. By providing examples of cartography in Chengdu and Sichuan, China, and using the locations of groundwater in fractures and pores when monitoring and managing red sandstone and mudstone wells, a series of maps of groundwater wells at different scales in the red beds of Chengdu was obtained. Most of the wells located in red beds are located in Jintang, Dayi, and Qingbaijiang and exhibit different cluster features. The kernel density estimation and spatial cluster analysis classification methods were used based on the Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) in three concentrated areas. This method describes the trends of the clustering results and the relationships between the locations of residents and red bed wells. The cartography results show that the groundwater wells in red beds are mainly distributed in hilly areas and partially correspond with the locations of villages and settlements, particularly their geological and topographic factors, which satisfy the maximum requirements of water use and recycling in Southwest China. The irrigation wells located in red beds are not only reliable and efficient but also replace inefficient water resources in the recharge-runoff-discharge groundwater process, which promotes the sustainable development of groundwater resources.

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“…An integral part of big data analytics is data mining (DM) that mine data to trace patterns, relationships between input and output variables, grouping similar data points, or forecasting future outcomes to make informed decisions [6]. Data mining is not limited to big data; it has been in use before the inception of big data, for example, clustering [7], regression [8] and classification [9]. Existing methods seem insufficient in analyzing big data due to multi-dimensional data having different data types and formats.…”