Energy efficient IoT-based cloud framework for early flood prediction

Abstract

Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy efficient flood prediction and forecasting system. IoT sensor nodes are constrained in terms of battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system's power consumption. cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e. water flow and water level, are being investigated for India's Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. To forecast the flood, the ANN (Artificial Neural Network) algorithm is used, and the simulation technique of Holt Winter is used to forecast the future flood. Data is obtained from the Indian government meteorological database and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.

Towards an Efficient Fog-Based Forest Fire Management Architecture

“…IoT is responsible for collecting real-time data in a socially collaborative manner, big data is an efficient approach to data analysis, fog computing brings fast processing capability to the system and reduces latency in the network when predicting floods in real-time, and cloud computing provides the powerful infrastructure for system management and long-term data storage and analysis. In (Kaur et al, 2021), researchers provided an energy-efficient IoT-based cloud framework for early flood prediction. The IoT framework uses a comprehensive historical dataset of environmental factors such as temperature, humidity, precipitation, and hydrological parameters like ñow and water level to predict flooding and related activities.…”

Technology of Internet of Things Responding to Natural Disasters

“…In the paper [61], the authors point out that in order to prolong the life of the system, an energy-saving method based on data heterogeneity can be adopted. A new preprocessing technology of flood detection system, principal component analysis (PCA), is proposed.…”

Review article: Current State of Deep Learning Application to Water-related Disaster Management in Developing Countries

“…Flood forecasting and rainfall prediction techniques have improved in the last few years due to the need to address immense economic and environmental losses caused by flood. Thirty-nine (39) out of forty-nine (49) articles explored application of deep learning techniques to flood forecasting, rainfall prediction and adopted various hydrologic modelling approaches like rainfall prediction (Yeditha et al, 2021;Chhetri et al, 2020;Endalie et al, 2021;Kumar et al, 2019), streamflow forecasting (Abbas et al, 2020;Kumar et al, 2004;Le et al, 2019;Loganathan & Mahindrakar, 2021), flood hazard and severity assessment (Kanth et al, 2022a;Kaur et al, 2021;Khosravi et al, 2020), rainfall-runoff modelling (Van et al, 2020) and flood susceptibility mapping (Bui et al, 2020). Interestingly, this is an indication that developing countries exhibit high flood vulnerability than developed countries, which have embraced better flood protection infrastructure, AI-informed water dynamics modelling, nature-based ecological solutions, efficient early warning systems, sustainable ecosystem services, sustainable urban design systems, and policies targeted at improving river health and monitoring.…”