Source Jouranl of CSCD
Source Journal of Chinese Scientific and Technical Papers
Included as T2 Level in the High-Quality Science and Technology Journals in the Field of Environmental Science
Core Journal of RCCSE
Included in the CAS Content Collection
Included in the JST China
Indexed in World Journal Clout Index (WJCI) Report
HUANG Jiming, LIU Runqing, WU Sizhan, QIN Hangdao, CHEN Jing. PREPARATION AND CHARACTERIZATION OF DEFECTIVE Zr-BASED METAL-ORGANIC FRAMEWORKS AND THEIR ADSORPTION PROPERTIES FOR TETRACYCLINE[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(3): 33-40. doi: 10.13205/j.hjgc.202403004
Citation: ZHAO Tianrui, LIU Yiming, LÜ Pengzhao, LI Yanliang, TANG Xiaomi, GUO Wei, ZHANG Jun, TIAN Yu. CONSTRUCTION OF LightGBM WASTE PRODUCTION PREDICT MODEL IN A SCENARIO OF ZERO-WASTE CITY[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(3): 210-215. doi: 10.13205/j.hjgc.202303028

CONSTRUCTION OF LightGBM WASTE PRODUCTION PREDICT MODEL IN A SCENARIO OF ZERO-WASTE CITY

doi: 10.13205/j.hjgc.202303028
  • Received Date: 2022-03-28
    Available Online: 2023-05-26
  • Publish Date: 2023-03-01
  • In 2018, China put forward a pilot project to construct Zero-Waste City. Through the investigation of the research area of one of the pilot cities, it was found that the domestic waste produced in this area showed a trend of rapid non-linear growth in recent years. Traditional predicting methods can no longer meet the expected delicacy management demand of local waste production. It is challenging to coordinate the development of local waste treatment capacity with the production increasing. Therefore, based on the urban multi-social data with the annual unit, a model research plan for predicting the overall waste production of the study area was proposed. Combining of the grey relational analysis algorithm and LightGBM machine learning algorithm can get several types of feature data in the multi-social data, which are most closely related to the growth of waste generation in the study area. The machine learning model construction and cross-validation tuning to obtain the domestic waste production prediction model with an MAE of 1.48 and an MAPE of 15.42%. Finally, the model predicted that domestic waste production in 2025 will reach 172,300 tons/year.
  • [1]
    SINGH J, LAURENTI R, SINHA R, et al. Progress and challenges to the global waste management system[J]. Waste Management & Research, 2014, 32(9):800-812.
    [2]
    马梦蝶. "双碳"目标下上海市"无废城市"建设的协同路径研究[J]. 智能建筑与智慧城市, 2022(1):37-39.
    [3]
    LIU B C, ZHANG L, WANG Q S. Demand gap analysis of municipal solid waste landfill in Beijing:based on the municipal solid waste generation[J]. Waste Management, 2021, 134:42-51.
    [4]
    LIVANI E, NGUYEN R, de NZINGER J, et al. A hybrid machine learning method and its application in municipal waste prediction[J]. Lecture Notes in Computer Science, 2013, 7987:166-80.
    [5]
    AYELERU O O, NTULI F N O F. Municipal solid waste generation and characterization in the city of Johannesburg:a pathway for the implementation of zero waste[J]. Waste Management, 2018, 79:87-97.
    [6]
    KARIMI-JASHNI. S A A. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate:a case study of Fars province[J]. Waste Management, 2016, 48:14-23.
    [7]
    WANG P Y, ZHANG H, JIANG Y, et al. Prediction of Municipal Solid Waste Production in Dalian[J]. Environmental Sanitation Engineering, 2019.
    [8]
    LIN K S, ZHAO Y C, TIAN L, et al. Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model:a case study of Shanghai[J]. Science of The Total Environment, 2021, 791:148088.
    [9]
    张旺. 基于Elman神经网络的城市生活垃圾清运量预测模型研究[D].武汉:湖北工业大学, 2017.
    [10]
    赵天瑞. 无废城市典型场景下的生活垃圾动态模型构建及运输模式优化[D].哈尔滨:哈尔滨工业大学.
    [11]
    曹飞飞. 灰色系统理论在粮食产量预测中的应用[J]. 数学的实践与认识, 2017,47(13):310-312.
    [12]
    ANDRES B, GORKA U, PEREZ J M, et al. Smart optimization of a friction-drilling process based on boosting ensembles[J]. Journal of Manufacturing Systems, 2018, 48:108-121.
    [13]
    姜丽蓉, 李卓, 杨晓芳. 基于多种时间预测模型的组合对成都市垃圾生产量的预测[J]. 黑龙江科学, 2021, 12(6):32-33.
    [14]
    李壮年, 储满生, 柳政根, 等. 基于机器学习和遗传算法的高炉参数预测与优化[J]. 东北大学学报(自然科学版), 2020,41(9):1262-1267.
  • Relative Articles

  • Cited by

    Periodical cited type(1)

    1. 张彪,石淇铭,白梓恒,刘泓宇,马涛,王芬,朱建锋,施佩. 氧化石墨烯辅助仿生矿化原位合成羟基磷灰石. 硅酸盐通报. 2024(01): 363-369 .

    Other cited types(2)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040510152025
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 12.5 %FULLTEXT: 12.5 %META: 84.7 %META: 84.7 %PDF: 2.8 %PDF: 2.8 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 18.5 %其他: 18.5 %其他: 0.4 %其他: 0.4 %上海: 2.8 %上海: 2.8 %保定: 0.8 %保定: 0.8 %北京: 2.8 %北京: 2.8 %十堰: 0.4 %十堰: 0.4 %南京: 0.8 %南京: 0.8 %台州: 1.2 %台州: 1.2 %合肥: 1.2 %合肥: 1.2 %哈尔滨: 0.4 %哈尔滨: 0.4 %大同: 0.4 %大同: 0.4 %天津: 2.0 %天津: 2.0 %太原: 0.4 %太原: 0.4 %威海: 0.4 %威海: 0.4 %宣城: 0.4 %宣城: 0.4 %常州: 0.8 %常州: 0.8 %常德: 0.8 %常德: 0.8 %广州: 1.2 %广州: 1.2 %庆阳: 0.8 %庆阳: 0.8 %延安: 0.4 %延安: 0.4 %张家口: 4.8 %张家口: 4.8 %忻州: 0.4 %忻州: 0.4 %成都: 0.8 %成都: 0.8 %扬州: 0.4 %扬州: 0.4 %昆明: 3.2 %昆明: 3.2 %晋城: 0.4 %晋城: 0.4 %朝阳: 0.4 %朝阳: 0.4 %杭州: 0.8 %杭州: 0.8 %武汉: 1.6 %武汉: 1.6 %江门: 0.8 %江门: 0.8 %济宁: 0.4 %济宁: 0.4 %海口: 1.6 %海口: 1.6 %深圳: 0.8 %深圳: 0.8 %温州: 0.8 %温州: 0.8 %湛江: 0.4 %湛江: 0.4 %漯河: 3.2 %漯河: 3.2 %濮阳: 0.8 %濮阳: 0.8 %石家庄: 0.4 %石家庄: 0.4 %福州: 0.8 %福州: 0.8 %秦皇岛: 3.2 %秦皇岛: 3.2 %芒廷维尤: 11.3 %芒廷维尤: 11.3 %芝加哥: 4.0 %芝加哥: 4.0 %衡阳: 0.4 %衡阳: 0.4 %衢州: 0.4 %衢州: 0.4 %西宁: 8.5 %西宁: 8.5 %西安: 6.5 %西安: 6.5 %贵阳: 0.4 %贵阳: 0.4 %达州: 0.4 %达州: 0.4 %运城: 0.8 %运城: 0.8 %遵义: 0.8 %遵义: 0.8 %邯郸: 0.4 %邯郸: 0.4 %郑州: 0.4 %郑州: 0.4 %重庆: 0.4 %重庆: 0.4 %银川: 0.8 %银川: 0.8 %长沙: 0.4 %长沙: 0.4 %驻马店: 0.4 %驻马店: 0.4 %黄冈: 0.4 %黄冈: 0.4 %其他其他上海保定北京十堰南京台州合肥哈尔滨大同天津太原威海宣城常州常德广州庆阳延安张家口忻州成都扬州昆明晋城朝阳杭州武汉江门济宁海口深圳温州湛江漯河濮阳石家庄福州秦皇岛芒廷维尤芝加哥衡阳衢州西宁西安贵阳达州运城遵义邯郸郑州重庆银川长沙驻马店黄冈

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (321) PDF downloads(9) Cited by(3)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return