# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 1 Introduction
(2022) introduced an optimized deep learning model integrating a convolutional neural network (CNN) with the BAT metaheuristic algorithm to predict daily streamflow in the Korkorsar catchment in northern Iran. This model outperformed the other algorithms [12]. Machine learning is a subset of artificial intelligence that exploits algorithms and statistical methods to provide computers with learning ability [13]. It aims to optimize experimental arrangements for a data structure [14]. A continuous source of data from actual observation is fed into the system, improving the learning over time. Artificial intelligence closely resembles how human brains capture internal data relationship patterns [15]. The acquired knowledge enriches the machine's ability to generalize a real-world position [16]. Metaheuristics denote high-level computational intelligence algorithm frameworks that are problem-independent and are employed to solve complex optimization demands [17]. A robust, iterative search process is involved in the metaheuristics algorithm to generate an approximation that does not guarantee an optimum solution [18] but instead an adequately good global solution within a reasonable computational time. The algorithm can self-tune the global exploration and local exploitation to reach greater search abilities [19]. Metaheuristics can be categorized into nature-inspired and non-nature-inspired. The nature-inspired category can be further classified into evolutionary algorithms [20] and swarm intelligence. Evolutionary algorithms include genetic algorithms, genetic programming, evolution strategy and differential evolution based on biological transformation. Swarm intelligence includes artificial bee colony algorithm, ant colony optimization, crow search algorithm, jellyfish search optimizer, firefly optimization and bat algorithm. The non nature-inspired category consists of the Jaya algorithm, imperialist competitive algorithm, simulated annealing, harmony search and forensic-based investigation algorithm. All evolutionary and swarm intelligence algorithms involve proper tuning of standard controlling parameters such as population size and generation boundary. In addition, each algorithm has its algorithm-specific control parameters such as mutation probability, crossover probability and selection operator for the genetic algorithm. Failure to properly tune can decrease computational speed and entrap in local optimal. Swarm intelligence algorithms are also subjected to slow convergence and are challenging to integrate with a particular artificial intelligence model [21]. In order to avoid algorithm-specific non-performance, the teaching learning-based optimization algorithm and the Jaya algorithm can be implemented [22]. The bat algorithm is used in tuning residential HVAC controller parameters to optimize energy consumption and obtain thermal comfort. It is also used for controlling illumination and air quality [23]. Other applications are wind power forecasting [24] and transportation [25]. The firefly algorithm has been used in numerous fields to solve complex applications such as breast cancer recognition, vehicle communication problem, path planning, privacy protection and forecast power consumption. It can also be used in structural optimization and image processing [26]. The Jaya algorithm has been developed for many engineering works such as structural damage identification [27], welding optimization, heat exchangers optimization, path selection for a wireless network, waterjet machines, dam monitoring [28], wind power systems and cart position control [29]. From the authors' observation, there is a lack of research in the area of optimization for deep learning using hybrid models. In order to fill this gap, this study aims to improve the deep learning model for better streamflow simulation and forecasting using optimization algorithm hybrid models, which will lead to a better early warning system. The contributions of this paper can be simplified as follows:  
1.