:

Marcos Pastorini1, Rafael Rodríguez2, Lorena Etcheverry1

  • 1Department of Computer Science, School of Engineering, Universidad de la República, Herreira y Reissig, 565, Montevideo 11300, Uruguay.

概括

本研究引入了一种机器学习框架,以填补缺少的环境数据,提高流域模型的准确性. 该方法有效地归因于气象,水量和质量数据,减少水资源管理中的不确定性.

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