Research On Reclaimed Water Resources Allocation Method Based On Ensemble Learning

Water resources are the most critical resource for the development of national economy and people's livelihood.However,with the increase of population and environmental pollution,the problem of global water shortage is becoming increasingly serious.Faced with this situation,people urgently need to find water sources that can replace tap water to ease water shortages.Reclaimed water resources have been hailed as "the second largest water source in cities".Once it is used rationally,it can alleviate the shortage of water resources to some extent.The allocation of reclaimed water resources is a key link in the rational use of reclaimed water resources,and has an important impact on the rational use of reclaimed water resources.The research of conventional reclaimed water resources allocation mostly adopts the combination of intelligent optimization algorithm and goal planning model.However,using only the traditional method to allocate water,the water allocation scheme is relatively single and lacks contrast,and it may be possible to obtain a water allocation scheme with a slightly inferior water allocation effect.The ensemble learning can integrate the water allocation schemes obtained by various methods,effectively enhance the applicability and accuracy of the model,and thus obtain a better water allocation scheme than the conventional method.Based on this advantage,this paper applies ensemble learning to the allocation of reclaimed water resources and proposes an allocation method of reclaimed water resources based on ensemble learning.This paper first studies the reclaimed water allocation model and user selection.The selection of models and users is a precondition for the allocation of reclaimed water resources based on ensemble learning.Therefore,it is proposed to use three criteria of critical distance,minimum economic scale,and pipe network utilization to select models and users.Secondly,studies Sample sets of reclaimed water resources allocation for ensemble learning.The allocation of reclaimed water resources to potential users,which does not exist historical water allocation data as sample sets of water allocation with ensemble learning.Based on this feature,This paper study how to use the genetic algorithm,artificial ant colony algorithm and particleswarm algorithm to form the allocation sample sets.This content provides technical support for the acquisition of ensemble learning water sample sets.Then,study the allocation of reclaimed water resources based on ensemble learning.With users as categories,the problem of reclaimed water allocation is transformed into a classification problem,which is incorporated into the scope of ensemble learning.Ada-SVM ensemble learning algorithm was chosen to support the allocation of reclaimed water resources.The framework of water allocation under the algorithm was established and the algorithm was designed.Finally,in order to verify the feasibility and effectiveness of the above-mentioned method of water resources allocation based on ensemble learning,a case study was conducted.Based on the content of Chapter 3,six users of water allocation were selected.A large number of water allocation schemes are obtained using the chapter 4.These water allocation schemes are treated as sample sets of water allocation with ensemble learning,and design and run program code according to the framework and algorithm in Chapter 5,finally get6 users' reclaimed water resources allocation results.Comparing the results of the allocation with the results of the conventional method,with water allocation costs and water shortage as the measurement standard,The water allocation effect of this paper'method is better,verify the feasibility and effectiveness of the method.