## 1 Introduction
Hence, the continued increase in ungauged basins in most African countries, especially in West Africa [10; 11], is concerning as most most gauges along River Niger are no longer functioning [10; 11]. To achieve the intent and purpose of monitoring river systems, there is a need to improve gauging systems or provide validated datasets that could serve as proxies for flow measures. This becomes essential to ensure a sustainably built environment, preserve crops through irrigation, and to an extent cushion the impacts of either flood or water scarcity [8]. The economic performance of most West African countries suggests that river monitoring is a manageable issue in terms of the scale of preference. Therefore, for the foreseeablefuture, the number of hydrological monitoring stations will likely remain the same across most rivers in West Africa. From the foregoing, there is the need to develop and design solutions that can help in some ways provide needed streamflow data for these data-scarce regions based on testing and validation of available datasets using improved statistical algorithms [10]. Prediction of streamflow has been implemented for numerous ungauged basins globally, employing diverse techniques and algorithms [12; 13; 14; 15; 16; 17]. [PERSON] et al. [18] showed that machine learning algorithms, emphasizing LSTM, performed better than traditional hydrological models. In their study on the prediction of floods using machine learning algorithms (MLAs), [PERSON] [12], using Non-linear Autoregressive Exogenous (NARX) and Support Vector Machine (SVM) algorithms affirmed that possibilities abound in the prediction of extreme events such as floods using MLAs. [PERSON] and [PERSON] [13] reported that a genetic algorithm model could help predict hourly discharge hydrographs using elevation data. Furthermore, [PERSON] et al. [14] in their study on ungauged catchments in Northern Thailand showed that the first pass approach algorithm provides an accuracy of 13-17 percent of the relative error for the monthly time step. These studies show the potential of MLAs as an aid in the prediction of streamflow. In a study aimed at comparing MLAs for discharge prediction [16], the authors used decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), RNN-LSTM, and CNN-LSTM predicted dam discharge. The study concluded that predicting the amount of discharge from a dam is possible. In another similar study, using combined learning models, the authors affirmed that combined MLAs can predict inflow through inflow learning, considering flow characteristics such as flow regimes [15]. In addition, [PERSON] et al. [17] in their study on resampling and ensemble techniques for flow forecast using random undersampling (RUS), random oversampling (ROS), and the synthetic minority oversampling technique for regression (SMOTER), and four ensemble techniques, randomized weights and biases, bagging, adaptive boosting (AdaBoost), and least-squares boosting (LSBoost), the authors concluded that resampling produces marginal improvements to high stage prediction accuracy, whereas ensemble methods produce more substantial improvements, with or without resampling. Other related studies include LSTM studies for river discharge in Indonesia [19], GRU prediction of river discharge in China [20], and TCN prediction of river discharge in the Yellow River, China. These studies have shown the possibility of predicted flow through testing and validation using available flow records. The focus of this paper is to consider some characteristics of river discharge such as low and high flows. The consideration of low and high flows in rivers of West Africa has received little attention despite the global relevance of low and high flows in hydrology [21]. In their paper, [PERSON] [21] affirmed that predicting low flows would go a long way in understanding catchment processes.