## 6 Conclusions  
Extant literature is limited to considering only the traditional relationship between environmental parameters and wetland water level for future water level predictions. For future projections, this study presents a novel concept using a temporal relationship between daily wetland water levels. Moreover, both traditional and temporal relationships were modeled using two types of ANN, and prediction performance was analyzed in detail. Several NN architectures were compared within the study, and lightweight, high-performing NN models for LSTM and FFNN were selected.  
The results confirmed that the temporal relationship within daily wetland water levels is much more robust in predicting future water levels than using the traditional relationship between environmental parameters and water level. The detailed analysis found that the degree of embedded relationship information within the wetland data is more critical than the volume of training data regarding water level prediction. Moreover, the normalized performance graph comparison presented that although the performance level substantially differs between FFNN and LSTM models, the prediction accuracy pattern between wetlands is similar. Hence, two prediction models learned two different relationships from wetland data in almost the same ratio from each wetland. The actual vs predicted water level graphs reconfirm the performance measured using the \(R^{2}\) Score, \(MSE\), and \(MAE\). The model can predict extreme cases like very high and low water levels, which is essential in the said application area. It also reconfirmed the greater generalization capability of the proposed LSTM model and its robustness against the uncertainties.  
However, ANN models are subjected to considerable deviation in predicting results out of the training data range. Hence it is required to accommodate a wider range of data for model training. Manual hyperparameter training is an exhaustive effort, and it is necessary to have expert knowledge compared to automatic hyperparameter tuning models. Compared to other techniques like GP that can perform UQ readily, it is comparatively difficult to accommodate uncertainty quantification for the ANN-based models due to limited training data and extensive time consumption.  
Future studies can investigate and select the most influenceable input parameters (i.e., variables) for water level predictions than using all seven identified input variables. Moreover, immediate effect analysis for water level variation from some selected input variables can be analyzed as an extended study. Finally, the comparative study will be extended to a comprehensive analysis of soft computing methods, including ANNs, GP, and other ML techniques for wetland water level prediction.  
Conceptualization, U.R.; methodology, M.H.; software, M.H.; validation, M.H.; formal analysis, M.H.; resources, U.R.; data curation, M.H.; writing--original draft preparation, M.H. and T.J.; writing--review and editing, Y.H. and U.R.; supervision, U.R.; project administration, U.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The meteorological and water level data used in this research work can be requested by corresponding authors only for research purposes.  
The authors acknowledge the support that they have received from the Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka, on data purchase.  
The authors declare no conflict of interest.