## 1 Introduction
It is necessary to consider at least 20-30 days of the previous day's water levels for accurate temporal relationship learning. [PERSON] and [PERSON] [29] have predicted the monthly average water levels in Sultan Marshes in Turkey using a multi-layer perceptron-type neural network model. They have investigated the traditional relationship between climate data (rainfall, temperature, evapotranspiration) and hydrological data (groundwater levels, spring flow rate, and the previous month's water levels). Although they have used several input parameters, including the last month's water level, the selected NN type will not support better temporal relationship learning. [PERSON] and [PERSON] [30] have carried out a study on the investigation of water level fluctuations in the Kerala Vembanad Wetland using ANNs. They have predicted one-day-ahead water levels using rainfall data and the previous day's water levels. The models' results were expressed using several numerical indices, such as the correlation coefficient and root mean square error. Also, they have found that neural networks only predict water levels accurately when there is information on wetland storage conditions. However, this study has not compared both traditional and temporal relationships, which is one of the major concerns. Moreover, it has considered only rainfall among possible environmental factors influencing the wetland water level. [PERSON] et al. [31] studied wetland water depth and area prediction in the Atreyee River basin using ANN and a non-linear regression model. In this regard, they have used Landsat satellite images of the river basin during pre- and post-monsoon seasons. There could be several drawbacks as they have used image processing data. Prediction accuracy would be limited with a sufficient number of satellite images. Also, images could be noisy because of the clad forming, dust, etc. Wetland water level measurements are restricted in most countries, especially Sri Lanka. This situation is mainly due to unawareness and lack of attention given by the local authorities. Also, fewer funding allocations for wetland developments have caused improper wetland management and maintenance. Therefore, many wetlands in Sri Lanka have faced severe problems, including insufficient water level measurements. Being the capital of Sri Lanka, wetlands in Colombo city have a significant threat including land degradation. According to recent research, wetland coverage in Colombo city has been reduced by 30% [32]. Wetlands act as flood detention basins. As the wetlands' special coverage is concentrated in Colombo, frequent flash floods are more prone to occur. The lack of water level measurements in Colombo wetlands is a major cause of inadequate flood management in the Colombo region. Also, less attention has been given to water level prediction in Sri Lankan wetlands. The first attempt to simulate the water levels in the Colombo flood detention area was taken recently by [PERSON] et al. [33]. However, it was an initial study to look at the relationship between water levels and climate parameters. Nevertheless, [PERSON] et al. have found the relationship between wetland water levels and climate parameters is highly non-linear [33]. Motivated by the importance of wetlands and monitoring their water level along with the power of deep machine learning techniques (i.e., ANN), this study extends our previous [33] study to investigate and compare traditional relationship and temporal relationships on an unbiased common ground for predicting wetland water level for the first time in this domain using two types of ANNs. In this study, hydrological data and meteorological data have been considered for wetland water level forecasting. In addition, artificial neural network models were developed to simulate the water levels at the Colombo Flood Detention area. As per the climate parameter prediction literature [34; 35; 36; 37], two fundamental types of existing relationships can be considered for predicting the water level of a wetland.