## 4 Discussion and Limitations
### Discussion of Specific Topics
4.2.3 Implications of Time Series Forecasting and Artificial Intelligence for Hydrologic Predictions  
The hydrologic domain has recently adopted time series forecasting and AI, thanks to technological developments that facilitate their implementation in a variety of fields. Five of the most representative papers on the implementation of time series forecasting and AI in the hydrological field will be discussed. Streamflow prediction was discussed by [PERSON] et al. [94], who developed an algorithm that estimates the nonlinear streamflow for hydrological stations in China. A parallel cooperation search algorithm (PCSA) and extreme learning machine (ELM) were tested. The PCSA method was optimized with multiple input-hidden weights and biases, obtaining better results than the ELM model. Initially, a large population was stored, then divided into multiple subswarms that would be further investigated using the PCSA algorithm. The domain of water resource management is very sensitive to disasters, and it is crucial to optimally predict the hydrologic metrics in order to minimize risks. [PERSON] et al. [95] explored multiple time series algorithms, a support vector machine, and a seasonal autoregressive moving average (SARIMA) for estimation of Pemali River Basin discharges in Indonesia. Using an autocorrelation function (ACF), the analysis demonstrated a correlation between historical values up to 12 months prior, which facilitated the prediction of future values and reduced the risks. In the hydrological domain, a crucial step is to optimally predict the streamflow. [PERSON] et al. [96] investigated the literature and implemented the non-tuned machine learning algorithm ELM. The model's effectiveness and reliability were tested on predicting one-step-ahead streamflow of the Johor River in Malaysia, using three time-scale datasets (daily, average weekly, and average monthly). The outcome demonstrated that ELM has higher accuracy than an artificial neural network (ANN) algorithm, with a higher R\({}^{2}\) value (0.94 compared to 0.90 for ANN) and a lower RMSE (2.78 compared to 11.63) and MAE (0.10 compared to 0.43). The authors recommended the ELM algorithm for streamflow prediction. Using a Least-Squares Support Vector Machine algorithm, decomposition reconstruction, and swarm intelligence, [PERSON] et al. [97] developed an improved method for hydrologic time series prediction. Four indexes that contained monthly information on two hydrologic stations in China were analyzed, resulting in multiple scenarios. The hybrid algorithm that contained signal swarm intelligence and decomposition reconstruction provided optimum results, improving the accuracy with an RMSE of 58.9%, compared to support vector machine and neural network algorithms. Water wave oscillation, a topic in the hydrological domain, was evaluated by [PERSON] et al. [98], detailing the preprocessing, modeling, and problems regarding wavelet analysis, such as the choice of mother wavelet, selection of temporal scale, uncertainty evaluation of the predicted wavelet, and differences between discrete and continuous wavelet methods. The authors explained the key differences between continuous and discrete wavelet algorithms, when they can be implemented, and what the main advantages are with time series forecasting, also taking into consideration the uncertainty. The hydrological domain benefits from AI models, which facilitate the implementation of solutions that predicts water wave oscillation, hydrologic prediction [97, 98], streamflow [94, 96], and discharge estimation [95]. Nevertheless, apart from the areas of applicability for time series forecasting and artificial intelligence, a focused discussion should be conducted on the evaluation metrics considered in these cases.