**Author details**

*Natural Hazards - Impacts, Adjustments and Resilience*

pertaining to flood events.

automation [47].

rence [47].

**7. Conclusion**

to prompt necessary action.

and precious investments.

machine or things to things interconnectivity [45]. In all interconnectivity of things and people, the internet remains the main driver. This interconnectivity of Things, enables the swift transmission of meteorological, hydrological and geological data

In flood disaster management, providing a quick feedback on the occurrence of floods can be a great step in preventing and mitigating flood disasters and their impact on livelihoods in society. Deploying IoT in flood management puts disaster managers at a position to create enhanced early warning systems that do not only measure the water levels and the speed of inundation, but early warning systems that could also send alerts to residents and flood managers through mobile phones and other personal electronic devices, and additionally, prescribe the best prevention and mitigation strategies based on data such as direction of runoff, speed of rise of water levels and the time at the disposal of residents to take necessary action. Big Data on the other hand, refers to "*the evolution and use of technologies that provide the right user at the right time with the right information from a mass of data that has been growing exponentially for a long time in our society*" [46]. Digital data collection has not only seen growth in volume but also in variety in storage formats, hence Big Data is often described as high-volume, high-velocity and/or high variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision making and process

Big data typically defines data that exceeds the storage, processing and comput-

Through Big data process automation, precipitation data, soil moisture data, temperature data, water content data of water bodies, data on evapotranspiration, ground water data, etc., are collected and processed in real-time without human supervision to make predictions and early warnings about flood disasters' occur-

ing capacity of conventional database [46]. Hence Big Data analytics typically involves automated software that assist in the collection, organisation and analysis of the data being generated to discover trends, correlations and other useful results

Flood disasters have had very devastating impacts on societies and have destroyed livelihoods and investments of staggering monetary value and importance to development. However, adequate involvement of technology are leading to the creation of people-centered early warning systems that enhances residents' awareness and preparedness to flood events to significantly reduce the adverse impacts of these disasters on people. This chapter discussed various aspects of flood disaster management including early warning systems, flood mitigation and adaptation strategies, the relevance of monitoring, evaluation and mainstreaming flood disaster management into national level development planning. The chapter again discussed and encourage the integration of advanced technological tools into the frontier of flood disaster management, as these tools have the capacity to capture, analyse and disseminate real-time flood data to all stakeholders to safeguard lives

**34**

Frank Jerome Glago Akatsi College for Education, Akatsi, Ghana

\*Address all correspondence to: frankjerome96@gmail.com

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
