Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R²) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

准确可靠的径流预测对于优化水资源管理、水库洪水调度、流域管理和城市水管理至关重要。许多研究人员已经发表了使用降雨径流模型、时间序列模型、数据驱动模型、人工智能等技术进行径流预测的文章。尽管如此，现实世界中仍需要通用的方法实践。这个问题的解决在于针对特定的研究领域选择不同的方法。本文使用支持向量回归机器学习模型来预测印度北阿坎德邦 Tehri 大坝每日和十日时间步长的流量。预测每日和十个每日时间步时考虑两种情况。第一种情况包括四个输入变量：流量、降雨量、温度和积雪面积。第二种情况仅包含三个输入变量：降雨量、温度和积雪面积。径向核用于克服数据集中的空间复杂度。 K 折交叉验证适用于预测，因为它在评估 SVR 模型在训练数据的各个子集上的性能后对预测错误率进行平均。收集了2006年至2020年每日和十个每日时间步长的流量数据。校准期为2006年至2016年，验证期为2017年至2020年。Nash Sutcliffe效率（NSE）和确定系数（R ^2））被用作本手稿中的准确性指标。当考虑三个输入变量时，在每日预测时间序列中观察到了滞后。对于其他场景，相应的模型在时间尺度和参数上都显示出出色的结果，这在预测中发挥着至关重要的作用。该研究还增强了机器学习模型中输入参数的潜在使用的影响。