TY - A2的他,小君非盟- Gunathilake Miyuru b . AU - Karunanayake Chamaka AU - Gunathilake,无尾目动物s . AU - Marasingha Niranga AU -萨马拉辛哈,Jayanga t . AU - Bandara Isuru m . AU - Rathnayake Upaka PY - 2021 DA - 2021/05/28 TI -水文模型和人工神经网络(ann)在斯里兰卡热带流域模拟水流SP - 6683389六世- 2021 AB -精确的流水量估计是必不可少的规划和决策的许多发展与水资源有关的活动。水文模型是一种经常采用和成熟技术来模拟河流相比,数据驱动的模型,如人工神经网络(ann)。此外,使用网络是最低的上下文中模拟水流斯里兰卡。因此,本研究提出了一个流水量估计从传统水文模型和安之间相互比较分析Seethawaka流域位于上游Kelani流域的一部分,斯里兰卡。水文模型是利用水文工程Centre-Hydrologic造型系统(HEC-HMS),而MATLAB中所开发的数据驱动的ANN模型。降雨和流速及流水量的数据2003 - 2010年期间一直使用。HEC-HMS是由四种类型的模拟输入降雨数据配置,包括实际降雨量数据集和三个卫星降水产品(SbPPs),即PERSIANN PERSIANN-CCS, PERSIANN-CDR。被训练使用的ANN模型三个著名的训练算法,即Levenberg-Marquadt (LM),贝叶斯正规化(BR)和共轭梯度(SCG)。结果显示,基于实际降雨量超过模拟水文模型的基于遥感SbPPs。BR算法ANN算法中被发现上级数据驱动模型的上下文中ANN模型模拟。 However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software. SN - 1687-9724 UR - https://doi.org/10.1155/2021/6683389 DO - 10.1155/2021/6683389 JF - Applied Computational Intelligence and Soft Computing PB - Hindawi KW - ER -