Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Samikwa, Eric; Voigt, Thiemo; Eriksson, Joakim

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00053

Cited by 22 publications

(15 citation statements)

References 25 publications

(27 reference statements)

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“…In another case study for Henan, China, Song et al (2020) utilized LSTMs for up to 10 hours of forecasts. In an attempt to build an IoT device that is able to forecast floods, Samikwa et al (2020) developed an LSTM network that works on a low-power device to forecast streamflow for up to 10 hours in Melbourne, Australia. As literature so far suggests, LSTM models for hourly streamflow forecasting are in abundance.…”

Section: Streamflow Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting

Sit¹,

Demiray²,

Demir³

2022

Preprint

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The volume and variety of Earth data have increased as a result of growing attention to climate change and, subsequently, the availability of large-scale sensor networks and remote sensing instruments. This data has been an important resource for data-driven studies to generate practical knowledge and services, support environmental modeling and forecasting needs, and transform climate and earth science research thanks to the increased availability of computational resources and the popularity of novel computational techniques like deep learning. Timely and accurate simulation and modeling of extreme events are critical for planning and mitigation in hydrology and water resources. There is a strong need for short-term and long-term forecasts of streamflow, benefiting from recent developments in data availability and novel deep learning methods. In this study, we review the literature for studies that employ deep learning in tackling tasks that are either to improve the quality of the streamflow data or to forecast streamflow. The study aims to serve as a starting point by covering the latest developments of deep learning approaches in those topics as well as highlighting problems, limitations, and open questions with insights for future directions.

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Section: Streamflow Forecastingmentioning

confidence: 99%

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting

Sit¹,

Demiray²,

Demir³

2022

Preprint

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“…However, the models were executed in the cloud-based server. Finally, Samikwa et al [ 28 ] utilized edge computing for flood prediction and carried it on a low-power device within the IoT wireless sensor network. Long short-term memory (LSTM), a type of ANN, was applied in the study to predict one-hour ahead-of-time forecasts of water level.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting

Liu

Yang

Wijaya

2022

Sensors

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Extreme weather events cause stream overflow and lead to urban inundation. In this study, a decentralized flood monitoring system is proposed to provide water level predictions in streams three hours ahead. The customized sensor in the system measures the water levels and implements edge computing to produce future water levels. It is very different from traditional centralized monitoring systems and considered an innovation in the field. In edge computing, traditional physics-based algorithms are not computationally efficient if microprocessors are used in sensors. A correlation analysis was performed to identify key factors that influence the variations in the water level forecasts. For example, the second-order difference in the water level is considered to represent the acceleration or deacceleration of a water level rise. According to different input factors, three artificial neural network (ANN) models were developed. Four streams or canals were selected to test and evaluate the performance of the models. One case was used for model training and testing, and the others were used for model validation. The results demonstrated that the ANN model with the second-order water level difference as an input factor outperformed the other ANN models in terms of RMSE. The customized microprocessor-based sensor with an embedded ANN algorithm can be adopted to improve edge computing capabilities and support emergency response and decision making.

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“…Several methods have been proposed and used in the literature found related to flood forecasting, in one such case, Samikawa [1] proposes an IoT based Flood Early Warning system running with the help of edge computing and Artificial Neural Networks (ANN). Water level and rainfall data are collected using Sensors and sent to an Arduino micro controller where the data is sent to a Raspberry PI which act as an edge computer via Bluetooth (BLE).…”

Section: Introductionmentioning

confidence: 99%

“…Since this research used large (from one year) amount of data to train the NARX which is a form of recurrent dynamic neural network with feedback connections encircling on several layers of the network, it has proven to provide predictions with lower error rate. Similar as [1], Using ANN based flood modeling and flood prediction. Acquiring the rainfall and water level data which then processed using ANN Radial Basis function which is a model of ANN architecture consisting of three layers naming Input, Hidden and output.…”

Section: Introductionmentioning

confidence: 99%

Flood Early Warning and Prediction System for Tributary Streams

2022

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Flood Early Warning system development is relatively new and costly area for developing countries, though it has captured attention of the respective parties since such Early Warning System can avoid loss of lives and reduce property damages from floods. Rather than standalone early warning system, warning system with the ability to forecast or predict flood events is more useful for the relevant stake holders where it can be used to plan and act fast. Geological areas around tributary streams are more likely to flood without considerable warnings from the nature as it highly depends on the main river behavior. This system uses Internet of Things (IoT) devices for data capture and transfer. River Water level, Rain status and the Water flow rate (discharge rate) is measured using Sensors. An Artificial Neural Network (ANN) is trained with collected data and integrated with live data feed in order to predict the water level. By doing so forecasting flood events according to the current readings. Notifications are sent via pre-defined notification channels. Due to the higher number of data types considered, ANN predicts water level with considerable accuracy. This collective approach of using ANN and IoT devices has made the forecasting easier and more reliable while using more variables made the predictions more accurate. In addition, these collected data can be used in the future for disaster recovery and mitigation planning since they are kept in cloud environment where public can access.

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Flood Prediction Using IoT and Artificial Neural Networks with Edge Computing

Cited by 22 publications

References 25 publications

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting

A Systematic Review of Deep Learning Applications in Streamflow Data Augmentation and Forecasting

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting

Flood Early Warning and Prediction System for Tributary Streams

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