## 5 Conclusions and Future Works  
In this paper, we have examined a fault-tolerant system for detecting and forecasting natural disasters based on IoT and ML called SENDI (System for dEtecting and forecasting Natural Disasters). The system was modeled with the aid of the ns-3 simulator and complied with the IoT standards available in the simulator. The MLP models were designed by Weka and the data collected by means of a WSN deployed in a real environment. A clustering system was adopted to address the failures of the system, such as loss of nodes or communication, giving SENDI a fault-tolerance capacity. In this system, nodes are rearranged on the basis of their remaining energy to keep the ability to continue doing forecasts. This clustering system was analyzed by means of experiments and achieved good results both improving the utilization of the total energy of the system and giving SENDI a fault-tolerant mechanism.  
A case study on flood forecasting was also included and used to analyze SENDI. In this case study, we used data collected from urban rivers by means of WSN to create forecasting models. These models were embedded in the SENDI nodes and used to generate alerts in a scenario where faults had previously occurred in various parts of the system. As a result, SENDI was able to make predictions with a high degree of accuracy even in an unfavorable scenario. In future work we intend to expand SENDI by analyzing the characteristics already described for the system (such as the forecasting models present in _Cloud_) and assigning new features such as the combination of readings from multiple nodes, even in the event of system failures. In addition, a distributed approach could be adopted to improve the accuracy of the forecasts, where multiple nodes share forecasts with each other. This could assist in distributing the workload and making combined forecasts with greater accuracy, as intended for the Tier 2. Moreover, a limitation of SENDI is the need for a large number of nodes, therefore, a study about the required number of nodes to create a robust and not easily degrade version of the system still needs to be done. Also, additional experiments using data present in other studies, as in [PERSON] et al. [13], as well as other leader election algorithms, for instance, TEEN (Threshold-sensitive Energy Efficient sensor Network), other machine learning techniques and enhanced tuning of the forecast models parameters should be performed to achieve a fairer and deeper comparison.  
**Acknowledgments:** [PERSON] would like to acknowledge the financial support granted by CAPES (process, 3752/15-1). [PERSON] would like to thank FAPESP (Sao Paulo Research Foundation, grant ID 2015/21642-6) and Office of Naval Research Global for funding his research project. [PERSON] would like to acknowledge the financial support granted by FAPESP (process, 2014/06330-5) in his research. Finally, this article is partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery Grant.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.