# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 1 Introduction  
The natural water movement on our planet is known as the hydrological cycle. Streamflow is one of the main components of this cycle. The streamflow characteristic is often associated with climate and land use conditions [1]. Under-capacity rivers can trigger frequent flooding in the surrounding catchment due to excess runoff. On the other hand, water scarcity can also happen during dry weather. Therefore, the state of streamflow can transpire in future events. Streamflow forecasting can optimize water resource allocation [2]. For this reason, researchers have been developing various methods to forecast streamflow [3]. The conventional approach relies on preserving mass, momentum and energy [4] to retrieve broad basin information. However, data collection is time-consuming and costly as the conventional method requires a wide range of parameters. As more and more flooding occurs due to climate change, a more accurate forecasting model is required to pursue better flood management and disaster preparedness [5]. Artificial intelligence is seen as a better alternative to the conventional method. A study has shown that the adaptation of artificial intelligence allows better river and drought management [1]. It can establish the association of predictors and predictand variables without considering hydrological complexity. Although many studies have shown promising results, standalone models (e.g., artificial neural network) display specific drawbacks of overfitting due to large datasets. In addition, past states of network retrieved from time-series data are not kept for the benefit of information related to data sequence [6]. These drawbacks can be tackled through the implementation of deep learning that can generate higher accuracy through better extraction of obscure data with higher computing power and complex mapping ability. This ability has contributed to significant developments in many fields, such as speech recognition, language processing and hydrological studies, such as river flood forecasting, runoff forecasting, streamflow forecasting and groundwater level forecasting [7]. [PERSON] and [PERSON] (2020) proposed a study applying a deep recurrent neural network, specifically the neural runoff model, to predict streamflow in the state of Iowa. The model successfully incorporated multiple measurements and model results to produce long-term rainfall-runoff modeling [7]. [PERSON] et al. (2021) applied a deep-learning hybrid model to forecast the monthly streamflow water level in the Murray Darling Basin that yielded improved results when optimized with Boruta [1]. [PERSON] et al. (2021) developed three components of the hybrid DIFFF-FFNN-LSTM model to forecast hourly streamflow, which accomplished better results than statistical methods [6]. [PERSON] et al. (2022) performed a comparison study between the stacked model of random forest and the multilayer perceptron algorithm with bidirectional LSTM. The bidirectional LSTM model significantly outperformed the stacked model for low-flow prediction [8]. [PERSON] et al. (2022) developed a study comparing four machine learning algorithms, namely random subspace, M5P, random forest and bagging, to predict streamflow in the Des Moines watershed. The M5P algorithm yielded the best prediction [9]. Increasing accessibility to the latest research has triggered tremendous advancement in science and technology. A modern measuring device can quickly secure physical hydrological data with standard intervals. As more significant obscured knowledge is extracted, more demands for complex engineering optimization start to the surface [10]. This requirement comes with multiple purposes, multi-level conditions and numerous restrictions. In response, more recent research has been integrating machine learning methods with a metaheuristic algorithm to solve the optimization complexity [11]. This integration leads to a more efficient, effective and robust search, resulting in faster convergence. [PERSON] et al.