## 5 Conclusions
### Future Directions  
Multimodal data integration with temporal modeling: Future research may focus on the integration of temporal sensor data (e.g., hourly turbidity, and hourly temperature trends) through sequence-aware architecture designs, such as hybrid models wherein LSTM and transformers are mixed or through temporal fusion transformers (TFT). These models, being capable of addressing time-dependent procedural changes in water quality, will also become useful for appraising very dynamic environmental prediction scenarios. Real-life effectiveness can be checked through benchmarking on time-stamped datasets. Recent computer vision research indicates that transformer variants may boast the ability to generalize across domains. [PERSON] et al. (2025) [PERSON] et al. (2025) implemented transformer-compatible data augmentation and instance-based segmentation methods in estimating the dimensions of buildings in urban scenes; [PERSON] et al. (2025) [PERSON] et al. (2025) incorporated advanced image enhancement techniques into transformer models of YOLOv8 to detect PPEs at construction sites. Though stemming from visual realms, the transformer-based approaches have much to offer in cross-domain adaptability and robustness, thereby implying that analog methods may also come in handy for environmental forecast studies such as water quality modeling. Cross-domain generalization with meta-learning: To tackle the regional variability for the water sources, meta-learning algorithms (for example, MAML or Reptile) can be exercised to enhance cross-site adaptability. Fine-tuning transformer models from a few-shot samples from new locations may enforce robust generalization. The performance should be tested on a dataset from geologically different regions, for example, urban vs. rural vs. coastal settings. Feature attribution via SHAP and integrated gradients: Whereas the interpretation of attention weights shed some light on the issue, the next steps should apply other model-agnostic interpretation methods, such as SHAP and integrated gradients to ETT and SAINT. These methods should then be able to uncover both global and local feature influences in artifacts as they would help clarify end-users the way parameters, such as pH, turbidity, or TDS, come into play in potable decisions. A comparison of interpretability methods could yield valuable insights as well. Model compression and edge deployment: Since monitoring water might eventually occur in remote or resource-starved locales, research would also develop model-compression techniques, including pruning, quantization, and knowledge distillation, for deploying these models onto edge devices with low power. Experiments may focus on the trade-offs between inference speed and model size, and predictive accuracy. Resistance to sensor interference and data loss: Deep models should also be examined in terms of the presence of noise, drops, and time delays in adding sensor data. Dropout methods, robust objectives, or possible Bayesian transformers (e.g., BERT with uncertainty estimation) could be applied to such conditions. Testing such under-constrained noise conditions will highlight the boundaries in operation. Open-source benchmark and standardized evaluation: Prior work also recommended that future research focus on the creation of open-source benchmarks for the classification of water quality. The opening up of the datasets, model implementations, and evaluation pipelines will go a long way toward enhancing experiment reproducibility and more consistent comparisons across studies. Conceptualization, S.O., O.P. and E.A. ; methodology, O.P. and H.C.; software, E.A. and S.E. ; validation, F.K., A.A. and S.O. ; formal analysis, O.P., F.K. and H.C.; investigation, S.O., E.A., O.P. and A.A.; resources, F.K., A.A. and S.E. ; data curation, H.C., F.K. and A.A.; writing--original draft preparation, H.C., A.A., S.E. and F.K. ; writing--review and editing, O.P. and E.A. ; visualization, S.O., H.C. and S.E. ; supervision, O.P., S.O. and E.A. ; project administration, S.O. and O.P.