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使用人工智能模型在中国图江河流域进行长期水需求预测,中国
Jun Shu1, Xinyu Xia2, Suyue Han1
1College of Management Science, Chengdu University of Technology, Sichuan, China.
PloS one
|May 22, 2024
概括
准确的水需求预测对于可持续的资源管理至关重要. 高斯过程回归 (GPR) 和基因算法优化的逆向传播神经网络 (GA-BP) 模型显示出高准确性,可以在各个部门预测未来的水需求.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 准确的水需求预测对于战略规划和有限的水资源的可持续利用至关重要.
- 有效的水资源管理是区域社会经济发展的基础.
研究的目的:
- 为了比较各种人工智能 (AI) 模型在不同用水部门的长期水需求预测中的适用性.
- 确定最有效的人工智能模型来预测未来的水需求在江河流域.
主要方法:
- 评估了五种人工智能模型:基因算法优化的逆向传播神经网络 (GA-BP),极端学习机器 (ELM),高斯过程回归 (GPR),支持向量回归 (SVR) 和随机森林 (RF).
- 模型使用2005-2020年的数据预测了农业,工业,家庭和生态部门的水需求.
- 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 来评估性能.
主要成果:
- 高斯过程回归 (GPR) 在农业 (R2=0.9811),家庭 (R2=0.9338) 和生态 (R2=0.9142) 水需求方面表现优越.
- 基因算法优化逆向传播神经网络 (GA-BP) 在工业用水需求方面表现最好 (R2=0.8580).
结论:
- 该研究确定了最佳的人工智能模型,用于不同部门的长期水需求预测.
- 这些经过验证的模型为加强可持续的水资源管理和规划提供了有价值的工具.
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