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 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. ANN (Artificial Neural Network) algorithm is used to predict the flood, and the simulation technique of Holt Winter is used to forecast the future flood. Data are obtained from the Indian government meteorological database, and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.
Highlights
Version of Record: A version of this preprint was published at Natural Hazards on July 13th, 2021
PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction
Principal Component Analysis is proposed as a novel pre-processing technique for the flood detection systems to reduce the dimensions of flood related attributes, and the resulting input representation is trained with Artificial Neural Networks (ANN) for classifying the data
Summary
Natural disasters are universal incident and require significant assistance to tackle Natural disasters such as earthquakes, hurricanes and floods are events that intensely impact wide zone, distressing population, affecting goods, and tremble the population on both economical as well as psychological viewpoint [1]. Flood prediction and detection is done using IoT sensors and cloud computing in a geographical area by providing proficient acquisition, processing and efficient storage of flood related information. Principal Component Analysis is proposed as a novel pre-processing technique for the flood detection systems to reduce the dimensions of flood related attributes, and the resulting input representation is trained with Artificial Neural Networks (ANN) for classifying the data.
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