A Review of the Internet of Floods: Near Real-Time Detection of a Flood Event and Its Impact

Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Early flood detection and rescue using bioinformatic devices, internet of things (IOT) and Android application

Purpose

The impact of natural disasters on human life, the environment and the flora and fauna can be contained to large extent by intelligent human intervention. This study introduces the human capabilities which can be extended considerably with technology. Internet of things have always provided opportunities for predicting and managing manmade/natural disasters. The extreme reason for causing soil erosions, landslides, cloud bursts, floods, etc., are due to excessive rainfall. However, the flood is one of the most happening natural disasters, following Bihar to be the most affected region due to floods. Lots of lives and properties were lost and damaged.

Design/methodology/approach

This implemented researchers to introduce an advanced solution for such calamities. Expectations were developed that it would signalize authority as early as possible so that advanced measures are taken before the effect. The lack of sensing or alarming technology in India pushed researchers to develop a model using the Android app that basically detected the upcoming flood and other calamities.

Findings

Most importantly the entire model was programmed with IoT and its techniques so that the response is quicker and more accurate.

Originality/value

This research study is original.

Research challenges and opportunities for using big data in global change biology

Global change biology has been entering a big data era due to the vast increase in availability of both environmental and biological data. Big data refers to large data volume, complex data sets, and multiple data sources. The recent use of such big data is improving our understanding of interactions between biological systems and global environmental changes. In this review, we first explore how big data has been analyzed to identify the general patterns of biological responses to global changes at scales from gene to ecosystem. After that, we investigate how observational networks and space-based big data have facilitated the discovery of emergent mechanisms and phenomena on the regional and global scales. Then, we evaluate the predictions of terrestrial biosphere under global changes by big modeling data. Finally, we introduce some methods to extract knowledge from big data, such as meta-analysis, machine learning, traceability analysis, and data assimilation. The big data has opened new research opportunities, especially for developing new data-driven theories for improving biological predictions in Earth system models, tracing global change impacts across different organismic levels, and constructing cyberinfrastructure tools to accelerate the pace of model-data integrations. These efforts will uncork the bottleneck of using big data to understand biological responses and adaptations to future global changes.