## 1 Introduction  
A wetland is a dynamic system where the land is saturated with water throughout the year, either entirely or partially [1]. Wetlands are characterized as transitional zones between primarily flooded deep water environments and well-drained hills [2]. They are among the most productive ecosystems in the world [3] and play a broad role in environmental, social, and economic aspects. Wetlands act as the kidneys of the landscape, filtering and capturing suspended particles, trapping pollutants such as carbon dioxide and nitrogen, and releasing clean water downstream [4]. They stabilize the global climate, thus acting as carbon sinks [5]. Wetlands accommodate floods, thus reducing flood risk during storms [6; 7]. Due to the extensive food chain and the abundant biodiversity, wetlands also have been treated as \"nature's supermarkets\" [8]. Pollutants such as nitrate in water sources are a significant concern, and wetlands are viable sinks for these pollutants [9; 10; 11]. Besides, they enhance water quality, control shoreline erosion, rejuvenate the ecosystem, recharge the groundwater table, facilitate recreation, and aesthetics, etc. [12; 13; 14]. Wetlands cover about 6% of the earth's surface [15]. Due to significant land changes due to urbanization, infrastructure development, and agriculture, these ecosystems are under extreme pressure [16]. Wetlands' vulnerability to climate stress has also been confirmed [17]. Therefore, the wetland degradation rate is drastically increasing. According to the 2021 Global wetland outlook report, \"Wetland coverage\" has been reduced by 35% since 1970 [18]. Since it is one of the most significant ecosystems, it is vital to protect such vulnerable ecosystems. The water level is the crucial parameter that defines the functionality of a wetland [19]. However, most countries still lack a proper mechanism for mapping and monitoring wetland water levels. Hence, water level projection will lead to the protection and proper management of the wetland. Wetland water levels change in response to variations in water inputs and outputs [20]. Rainfall, surface water inflows, and groundwater inflows are examples of water inputs to wetlands, whereas evaporation, surface water outflows, and seepage are examples of water outputs [21]. Water levels link the wetland's hydrological, meteorological, and geomorphological characteristics to the surroundings [22]. In addition, wetlands are highly vulnerable to changes in the atmosphere. As the air temperature increases, rainfall patterns change and evaporation increases, causing the wetlands to swell [23]. On the other hand, as the temperature rises, relative humidity falls [24; 25]. In addition, high wind speed cause more evaporation in the water bodies, lowering their water levels [26]. [PERSON] [27] used neural networks with backpropagation to forecast Lake Van, Turkey, water levels. They have uncovered that the neural networks give accurate results even though the relationships between the rainfall and consecutive water levels are highly dynamic. They have trained the neural network by having three different arrangements in the input nodes. All three models have produced relatively similar results. One of the significant drawbacks of this study is that they have yet to consider the rest of the meteorological parameters other than the rainfall. [PERSON] et al. [28] emphasized the significance of predicting wetland water levels while pointing out the difficulties due to the data limitations. They have predicted the water levels in the Upo wetland, South Korea, using several machine-learning techniques, including ANN, decision trees (DT), random forest (RF), and support vector machines (SVM). In addition, researchers have considered the temporal relationship for water level prediction. However, considering only the previous day's water levels will not be adequate for better temporal information extraction through NNs.