Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

预测和扩展水流是水文学的一个重要方面，特别是在由于各种原因无法在当地获取时间序列的情况下。本研究评估了使用机器学习技术进行预处理、模型微调、特征选择或采样以增强水流预测的预测结果的必要性。在这方面，利用不同站的各种月降雨量和径流时间序列数据对Pol-Chehr站的月径流量进行了分析。 k 折交叉验证器方法中的流预测结果通常优于时间序列方法，除非使用未经预处理或特征选择的原始数据。将简单的 SVR 模型应用于原始数据会导致最弱的结果，但将 GA-SVR 模型应用于原始数据会显着增加纳什系数约 215% 和 72%，将 k 中的 NRMSE 降低约 48% 和 36% -折叠和时间序列方法，即使没有特征选择。另一方面，标准化在 k 倍方法和时间序列方法中都能产生高度准确的模型预测，在简单 SVR 模型的测试期间，最小纳什系数分别为 0.83 和 0.73。最后，使用 GA 等优化算法来微调 ML 模型和特征选择并不总能提高预测精度，而是取决于选择原始数据还是预处理数据。总之，结合 k 折交叉验证器和预处理通常会产生高度准确的预测结果，R值超过 93.7%（Nash = 0.83，SI = 0.55，NRMSE = 0.09），而不需要任何额外的微调或优化。仅当同时使用 TS 方法时，使用特征选择才有意义。