Streamflow plays a major role in the optimal management and allocation of available water resources in each region. Reliable techniques are therefore needed to be developed for streamflow modeling. In the present study, the performance of streamflow modeling is improved via developing novel boosted models. The daily streamflows of four hydrometric stations comprising of the Brantford and Galt stations located on the Grand River, Canada, as well as Macon and Elkton stations respectively, located on the Ocmulgee and Umpqua rivers, United States, are used. Three different types of boosted models are implemented and proposed by coupling the classical multi-layer perceptron (MLP) with the optimization algorithms, including particle swarm optimization (PSO) and coupled particle swarm optimization-multi-verse optimizer (PSOMVO) and a time series model, namely the bi-linear (BL). So, the boosted MLP-PSO, MLP-PSOMVO, and MLP-BL models are developed. The accuracy of all the boosted models is compared with the classical MLP and BL by the statistical metrics used. It is concluded that all the boosted models developed at the studied stations lead to superior modeling results of the daily streamflows to the classical MLP; however, the boosted MLP-BL models generally outperformed the MLP-PSO and MLP-PSOMVO ones.

流量在每个区域的可用水资源的优化管理和分配中起着重要作用。因此，需要开发可靠的技术来进行流量建模。在本研究中，通过开发新颖的增强模型可以改善流模型的性能。使用了四个水文站的日流量，这些水文站包括位于加拿大大河的布兰特福德站和高尔特站，以及分别位于美国奥克姆吉河和安普夸河上的梅肯站和埃尔克顿站。通过将经典的多层感知器（MLP）与优化算法耦合来实现和提出三种不同类型的增强模型，包括粒子群优化（PSO）和粒子群优化-多诗词优化器（PSOMVO）和时间序列模型，即双线性（BL）。因此，开发了增强型MLP-PSO，MLP-PSOMVO和MLP-BL模型。通过使用的统计指标，将所有增强模型的准确性与经典MLP和BL进行了比较。得出的结论是，在研究站开发的所有增强模型均导致每日流向经典MLP的卓越建模结果；但是，增强型MLP-BL模型通常优于MLP-PSO和MLP-PSOMVO模型。