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Industrial Water Consumption Prediction Improved by SSA-SVM Based on Mixed Strategy
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Jing-chun CAO1, Min LU2
Water Resources and Power | 2023, 41(9) : 28 - 31
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Water Resources and Power | 2023, 41(9): 28-31
• HYDROLOGY, WATER RESOURCES AND ENVIRONMENT •
Industrial Water Consumption Prediction Improved by SSA-SVM Based on Mixed Strategy
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Jing-chun CAO1, Min LU2
Affiliations
  • 1.Yunnan Institute of Water Resources and Hydropower Survey, Design and Research, Kunming 650000, China
  • 2.College of Water Resources and Hydraulic Engineering, Yunnan Agricultural University, Kunming 650000, China
Published: 2023-09-25 doi: 10.20040/j.cnki.1000-7709.2023.20222434
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In order to realize the prediction of industrial water consumption and improve the prediction accuracy, a mixed strategy was introduced to optimize the sparrow search algorithm (SSA) for improving the global search ability. The improved sparrow search algorithm (ISSA) was used to optimize the parameters of support vector machine (SVM). A support vector machine model (ISSA-SVM) based on hybrid strategy and ISSA was established, and the prediction of industrial water consumption in Ningxia was taken as an example. The results show that the ISSA-SVM model has the characteristics of fast optimization speed and high precision, and it has good applicability and feasibility in industrial water consumption prediction.

support vector machine  /  industrial water consumption forecast  /  sparrow search algorithm  /  adaptive inertia weight  /  criss-cross strategy
Jing-chun CAO, Min LU. Industrial Water Consumption Prediction Improved by SSA-SVM Based on Mixed Strategy[J]. Water Resources and Power, 2023 , 41 (9) : 28 -31 . DOI: 10.20040/j.cnki.1000-7709.2023.20222434
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Year 2023 volume 41 Issue 9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222434
  • Receive Date：2022-11-18
  • Online Date：2026-01-28
  • Published：2023-09-25
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History
  • Received：2022-11-18
  • Revised：2022-12-09
Affiliations
    1.Yunnan Institute of Water Resources and Hydropower Survey, Design and Research, Kunming 650000, China
    2.College of Water Resources and Hydraulic Engineering, Yunnan Agricultural University, Kunming 650000, China
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表12种不同金属材料的力学参数
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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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