Research on Spatial Distribution Prediction and Treatment Facility Location of Greenery Waste Based on Machine Learning and VRP Models:A Case Study of Wuhan City
The system of landscaping waste treatment facilities is the fundamental link for its resource utilization and recycling.The research takes Wuhan as an example to study the spatial distribution of greenery waste and the location of treatment facilities.For the former,based on big data such as high-definition satellite pictures,street view images,street tree spatial points,etc.,and using classification(convolutional neural network),clustering(K-means clustering),regression prediction(polynomial regression),the reasonable calculation of daily and peak greenery waste volumes in current and planning areas is realized.For the latter,the research adopts ALNS algorithm in vehicle routing problems,which incorporates constraints such as closest distance,minimum transportation turnover,limited time window,and limited facility capacity.It intelligently selects four types of facilities,including cluster collection points,nearby consumption stations,comprehensive processing plants,and centralized transfer stations,as well as related lines.The results show that the daily output of landscaping waste in the study area is 327,100 tons/year,with a peak output of 70,000 tons,similar to the situation in cities of the same level.It is recommended to layout 200 cluster collection points,50 nearby consumption stations,4 comprehensive treatment plants,and 14 centralized transfer stations to improve collection and transportation efficiency and achieve regional"production income"balance.
landscape architecturegreenery wastespatial distributionsite selection of treatment facilitiesmachine learningvehicle routing problem(VRP)Wuhan City
NETL
NSTL
万方数据
