## 2 Related Works  
In [PERSON] et al. [9] confirms the feasibility of using IoT along with Crowdsourcing for applications aimed at disaster response, together with the challenges this raises. In this paper, the authors describe how during a disaster, such as an earthquake, the users can retain a reduced but usable part of their capacity for communication (despite the destruction of basic facilities), for instance, by using SMS messages. Moreover, as well as the information that the users send, this type of communication contains extra data, such as longitude and latitude coordinates, that can help in the disaster response. A case study was undertaken which involved interviewing selected members of the community,This information was used to identify data sources, both in IoT and crowdsourcing, which could help in the disaster response. [PERSON] et al. [6] conducted another study on disaster management aided by social networks. His work attempted to design an IoT environment to represent the dynamics of communication and interaction of objects. The model was formed through a process of recognizing and categorizing the properties of smart objects and conducting an analysis of social networks. Since there is a lack of a specialist database for disasters, the model was validated by means of a graph theory combined with the analysis by the CPAN database of the Perl development community. [PERSON] et al. [10] propose the use of IoT DfPL (Device-free Passive Location), since this enables the users to be located in an environment without the need to have any type of device for disaster management. In their work, the IoT DfPL consists of a WSN DfPL that collects the data. The collected data is sent via the Internet and Analyzed; it thus requires the presence of people in the monitored area during the occurrence of a disaster. Unlike the procedure adopted in this work, at no stage are the data processed in the nodes, even if there is a loss of communication with the base station. Moreover, as in the case of the studies of [PERSON] [9] and [PERSON] [6], [PERSON] [10] does not make use of a real database with data collected in the targeted environment. Another point which differs from this paper, is the lack of a mechanism for forecasting disasters. With the use of ML for disaster forecasting, precious minutes can be spent on decision-making, which can assist in reducing the loss of materials and lives, even in the event of a node failure. [PERSON] et al. [11] set out a mixed architecture that combines WSN with multiple types of sensors and _Cloud_ computing for the forecasting of natural disasters. In their study [PERSON] et al. establish an SaaS (Software as a Service) framework that is integrated with _Cloud_-WSN; this is able to provide virtualization both at the level of the nodes and at the level of the network. The virtualization is delivered with the aim of improving the use of WSN resources. The data are collected by the WSN and sent to the _Cloud_ for a climate analysis; these data and results are shared with the authenticated users in the system and warnings are issued if any kind of danger is foreseen. However, in their study [PERSON] et al. failed to foresee new trends in technology such as IoT either in their architecture or in the use of some kinds of fault-tolerance mechanism if there is a loss of nodes or communication. This is because in their scheme they only work with a simple WSN. Following the _Cloud_ computing idea, [PERSON] et al. [12] show a survey focusing on the performance of different _Cloud_ datacenters also presenting an interesting approach to evaluate these performance parameters without being restricted to provider information. [PERSON] et al. [13] present a project very similar to the one developed in this paper, which uses WSN, IoT and ML for flood forecasting, but is flood-only and does not take into account fault tolerance. As model for flood prediction, [PERSON] et al. [13] used RNA and ns-2 for the simulation. As experiments and results, [PERSON] et al.