Hierarchical Coloured Petri-Net Based Multi-Agent System for Flood Monitoring, Prediction, and Rescue (FMPR)

Akhtar, Nadeem; Rehman, Abdul; Hussnain, Mujtaba; Rohail, Saad; Missen, Malik Muhammad Saad; Nasir, Mehwish; Hayder, Alina; Salamat, Nadeem; Pasha, Maruf

doi:10.1109/access.2019.2958258

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…We have a solid background in mathematical analysis, design, specification, modeling, and verification of different distributed systems. Our previous expertise on ensuring correctness properties of safety and liveness for autonomous robotic multiagent system [57][58][59] and flood monitoring systems [30,[60][61][62] has helped us in the formal modeling and proving of this flood-WMR SoS.…”

Section: Discussionmentioning

confidence: 99%

Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Rehman

Akhtar

Alhazmi

2021

Scientific Programming

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Floods after monsoon rains are frequent disasters that affect millions of lives in Pakistan. Human lives are lost, agriculture economies are destroyed, and livestock animals, houses, fruit farms, and crops are lost which are the major livelihoods of thousands of people in Punjab. Each year there are heavy rains in the monsoon season and, due to global warming, there is the rapid melting of snow in northern glaciers; these factors subsequently cause floods. There is also loss of life due to the spread of waterborne diseases and snake bites. Flood monitoring provides early detection of a flood and the calculation of its intensity, which results in reduced human life losses and economic losses. Most casualties are caused by the lack of timely real-time, authentic information about the high-risk areas, and flood intensity, speed, and direction. Therefore, the proposed approach is centered on formal modeling and verification of safety and liveness properties of flood monitoring perceivers. Each flood perceiver has several sensors. It requires the collection of information starting from the flood perceiver, observer, and environmental forecast. This information is processed to determine the flood intensity level. We have developed a CP-Nets’ formal model and model-checked it. We have verified the safety and liveness properties of correctness by exhaustive verification of the system using model-based proof obligations (Event-B method using Rodin). Our objective in this research is to propose a correct, reliable, and efficient flood warning, monitoring, and rescue (WMR) SoS based on formal methods. We have used formal modeling and model-checking based on state-of-the-art hierarchical CP-Nets supported by exhaustive formal proof obligations of Event-B.

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Section: Discussionmentioning

confidence: 99%

Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Rehman

Akhtar

Alhazmi

2021

Scientific Programming

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“…where r 2 i represents the input noise power, n represents the size of the median filtering mask, and f ð nÞ represents the function of the noise density [16]. The noise variance in the average filter is given below:…”

Section: Noise Reducing Performance Of the Median Filtermentioning

confidence: 99%

Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network

Dhaya¹,

Kanthavel²

2022

Intelligent Automation &Amp; Soft Computing

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The high prevalence of urban flooding in the world is increasing rapidly with the rise in extreme weather events. Consequently, this research uses an Automatic Flood Monitoring System (ARMS) through a video surveillance camera. Initially, videos are collected from a surveillance camera and converted into video frames. After converting the video frames, the water level can be identified by using a Histogram of oriented Gradient (HoG), which is used to remove the functionality. Completing the extracted features, the frames are enhanced by using a median filter to remove the unwanted noise from the image. The next step is water level classifiers using a Convolutional Neural Network (CNN), which is utilized to classify the water level in the images. The performance analysis of the method is analyzed by various parameters. The accuracy of the proposed method is 11% higher than that of the k-Nearest Neighbors (KNN) classifiers and 5% higher than that of the ANN classifiers, and the processing time is 7% less than that of the KNN classifiers and 4% less than that of the Artificial Neural Network (ANN) classifiers.

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“…Scales are available in increments as large as 1164. Large datasets were utilized to accomplish what has been outlined above (Akhtar et al, 2019).…”

Section: Table 1 Average Growths and Ripening Of The Cotton Plantmentioning

confidence: 99%

Machine learning-based Prediction of Cotton farming using ARIMA Model

Malik

Umar

2021

iRASD J. Comp. & Info Tech.

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Disease detection in agriculture is critical in severe weather conditions. Keeping an eye on many cotton fields is challenging for farmers. Deep learning and image processing identify agricultural ailments before they spread and further damage. Cotton crop with the most important correlation quantities among pest disease existence are inspected by predictors. In this study, transfer learning methods for analysis are checked. The machine learning approach developed accurately distinguishes healthy and unhealthy plants. As a result, the proposed model may be used to monitor a wide range of areas for more rapid analysis and action, leading to increased productivity. It is possible to anticipate a timeline using ARIMA. A long-term study reveals that the value swipes widely during short and long periods due to random influences. It is also inspected how the recommended approach is affected by using several cotton crop characteristics. In addition to separating green crops from other crops in the field, this procedure can also separate harvests such as Cotton from other crops.

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Hierarchical Coloured Petri-Net Based Multi-Agent System for Flood Monitoring, Prediction, and Rescue (FMPR)

Cited by 13 publications

References 34 publications

Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Formal Modeling, Proving, and Model Checking of a Flood Warning, Monitoring, and Rescue System-of-Systems

Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network

Machine learning-based Prediction of Cotton farming using ARIMA Model

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