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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.

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为实现工业用水量预测和提高预测精度，引入混合策略对麻雀搜索算法（SSA）进行优化以提高全局搜索能力，采用改进后的麻雀搜索算法（ISSA）优化支持向量机（SVM）参数，建立基于混合策略改进麻雀搜索算法优化的支持向量机模型（ISSA-SVM），并将该模型应用于宁夏的工业用水量预测中。结果表明，ISSA-SVM模型寻优速度快、训练和预测精度高，将其应用于工业用水量预测中具有良好的适用性和可行性。

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曹敬椿（1999-），男，硕士研究生，研究方向为水文与水资源工程，E-mail：2457902757@qq.com

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曹敬椿（1999-），男，硕士研究生，研究方向为水文与水资源工程，E-mail：2457902757@qq.com

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