Design of an IoT-based Flood Early Detection System using Machine Learning

Mousavi, F; Yousefi, Saleh; Abghari, Hirad; Ghasemzadeh, Ardalan

doi:10.1109/csicc52343.2021.9420594

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…In the lower portions of the Semarang region, Widiasari et al [27] employed an LSTM model to forecast river water levels. Moreover, a flood early detection system-based IoT was proposed by Mousavi et al [28]. In this work, the authors employed various ML and DL algorithms for real-time flood monitoring and detection.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

et al. 2023

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Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

et al. 2023

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“…They predict the surge levels after 5, 12, and 24 h with the help of hydrodynamic and meteorological data from the Tottori coast, Japan. Several different neural network models are evaluated against a real-world dataset in Pennsylvania but in a simulation environment where it is concluded that an LSTM-based approach has the best result on predicting downstream flow rate using upstream rainfall data [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

High-Precision AI-Enabled Flood Prediction Integrating Local Sensor Data and 3rd Party Weather Forecast

Wang

Abdelrahman

2023

Sensors

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Flooding risk is a threat to many sea-level cities and residential areas in the world. In the city Kristianstad in southern Sweden, a large number of sensors of different types have been deployed to monitor rain and other weather conditions, water levels at sea and lakes, ground water levels, and water flows in the city’s storm-water and sewage systems. All the sensors are enabled by battery and wireless communication, and allow real-time data to be transferred and visualized on a cloud-based Internet of Things (IoT) portal. To better enable the system with capacity of foreseeing upcoming flooding threats and to allow early response from decision-makers, it is desired to build a real-time flood forecast system by utilizing the massive sensor data collected at the IoT portal and data from 3rd party weather forecast service. In this article, we have developed a smart flood forecast system using machine learning and artificial neural networks. The developed forecast system has successfully integrated data from multiple sources and can make accurate flood forecast at distributed locations for the coming days. After being successfully implemented as software product and integrated with the city’s IoT portal, our developed flood forecast system has significantly extended the basic monitoring functions of the city’s IoT infrastructure. This article presents the context of this work, the challenges that have been encountered during our development, our solutions and performance evaluation results. To the best of our knowledge, this is the first large-scale IoT-based real-time flood forecast system that has been enabled by artificial intelligence (AI) and deployed in real world.

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“…The author proposed IoT based approach, to cover wide-area and reliability LoRaWAN method is used. For forecasting the occurrence of flood GRU neural network, LASTM and ANN model is used [Mousavi et al 2021].…”

Section: Related Workmentioning

confidence: 99%

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

Dubey

Katarya

2021

Preprint

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Disaster is a very serious dissipation that arises for a short time period, but the impact of that disaster on human society is very dangerous and very long-lasting. Disasters are categorized into two types like natural disasters and manmade disasters. Among all disasters, of all the natural disasters, flood is the commonplace natural disaster. Flood disaster that causes huge loss of human life, diversity as well as economic loss, which is very dangerous for the developing countries and developed countries also. Nowadays during the monsoon season flood is dangerous for all the geographical areas located nearby water bodies. Much research has been done for flood detection. Machine Learning and many other recent technologies are playing a vital role in predicting the occurrence of floods. For prediction purposes, a huge amount of data is requiring collected from sensors deployed in various locations. In this paper, we used the Batch normalization with Deep Neural Network (BNDNN) technique for the classification of data in three classes as Low, Moderate, and High. The result obtained from our proposed model is compared with some other models like Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). In this our proposed BNDNN provides 89% accuracy which is higher among all existing models. Models are compared based on some parameters like Accuracy, Precision, Recall, F –Score. The compression among all the models used in this paper shows that our proposed model provides better results.

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Design of an IoT-based Flood Early Detection System using Machine Learning

Cited by 15 publications

References 12 publications

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

High-Precision AI-Enabled Flood Prediction Integrating Local Sensor Data and 3rd Party Weather Forecast

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

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