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

ZHAO Tianrui; LIU Yiming; LÜ Pengzhao; LI Yanliang; TANG Xiaomi; GUO Wei; ZHANG Jun; TIAN Yu

doi:10.13205/j.hjgc.202303028

Volume 41 Issue 3

Mar. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(3): 210-215

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

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

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

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

doi: 10.13205/j.hjgc.202303028

State Key Laboratory of Urban Water Resources and Water Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China

Received Date: 2022-03-28
Available Online: 2023-05-26
Publish Date: 2023-03-01

Abstract

Abstract

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.
- zero-waste city,
- domestic waste,
- production predict,
- grey relevance analysis,
- lightGBM

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References(14)

References

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