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FLOOD PPT 1.pptx

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The document describes a proposed advanced flood detection system based on sensor technology and machine learning algorithms. The system would use sensors to collect data on water levels and other environmental factors. This data would be processed using an Arduino and classified using a random forest machine learning algorithm to detect flooding. If flooding is detected based on threshold values, alerts would be sent to users through a cloud-based IoT platform to warn of the flood risk in real-time. The system is intended to provide low-cost flood monitoring and warnings.

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Advanced Flood Detection System based on Sensor Technology and Machine Learning Algorithm Guided By: Prof. Kanchan Mahajan Sandip Foundation's​ Sandip Institute of Technology and Research Centre, Nashik Department of Computer Engineering​ Presented By: • Akshay Jadhav [B150614244]​​ • Kshitija Khadke [B150614256]​ • Shraddha Gite [B150614238]​
OUTLINE • INTRODUCTION • OBJECTIVES • PROBLEM STATEMENT • SCOPE OF PROJECT • ARCHITECTURE • REQUIREMENTS • ALGORITHM • UML DIAGRAMS • LITERATURE REVIEW • REFERENCES
INTRODUCTION • Floods are a major catastrophic event that allows for the catastrophic destruction of any nation, affecting human lives and making outrageous harm to people and properties. • Without proper monitoring and effective mitigation measures, these natural perils often culminate in disasters that have severe implications in terms of economic loss, social disruptions, and damage to the urban environment. • The aim is to build an efficient flood warning system while maintaining reasonable production cost has been a meaningful mission for many researchers • In this system, a cluster of servers will collect and process data from the hydrological observation stations in real time and with the help of Random Forest Algorithm for the classification of data the available results can be displayed on client computer by remote access and issue warnings if there is a risk of flood with the help of IoT.
OBJECTIVE  The main objective of this project is to develop and design a flood detection system that will detect flood automatically and transmit data through IoT.  In the first stage, system will detect the current water levels on flood by taking sensor values from outside environment and it will give real-time information to the appropriate station about severity.  At the second stage, the machine learning algorithm Random Forest is executed for classification and examine the level of flood, information to evaluate if the level of water is typical or in unsafe condition with the help of threshold values.  The proposed system is a low cost in design and easy for maintenance. This project will update the water level at the web server and the system will issue an alert signal to the citizens for evacuation so that fast and necessary action can be taken.
PROBLEM STATEMENT • Many flood warnings stations have been developed and installed in prosperous countries but the manufacturing cost is usually too high to be practical in developing countries. • Therefore, building an efficient flood warning system while maintaining reasonable production cost has been a meaningful mission for many researchers including our project.
SCOPE OF THE PROJECT • Historical records have shown that flood is the most frequent natural hazard, accounting for 41% of all-natural perils that occurred globally in the last decade. In this period alone (2009 to 2019), there were over 1566 flood occurrences affecting 0.754 billion people around the world with 51,002 deaths recorded and damage estimated at $371.8 billion. Put in context, these statistics only account for “reported” cases of large-scale floods. • The ultimate goal was to improve the prediction accuracy, for this purpose some researchers have explored the correlation among weather features and prediction accuracy and tried to find the best combinations of those features to tune the performance. • Few researchers on the other hand worked to train the mining technique well to achieve the high accuracy in prediction. Few have compared the modern techniques with the conventional ones. • Nevertheless, the current situation calls for improved ways of monitoring and responding to floods. The importance of improved flood monitoring cannot be overemphasized given the growing uncertainty associated with climate change and the increasing numbers of people living in flood-prone areas.
ARCHITECTURE
• To detect a flood the system observes various natural factors, which includes humidity, temperature, water level and flow level. To collect data of mentioned natural factors the system consists of different sensors which collects data for individual parameters. • For detecting changes in humidity and temperature the system has a DHT11 Digital Temperature Humidity Sensor and for the water level measurement it has water level sensor WL400. • The system has a wi-fi connectivity, using the ESP8266 Wifi module, which connects the system to cloud; thus, it’s collected data can be accessed from anywhere quite easily using IoT. • All these sensors are then connected to ARDUINO UNO, which processes and saves data. • The data movement from sensors to application can be viewed as a series of layered architecture that consists of perception layer, network layer and application layer. • The perception layer consists of various devices involved in getting the geological data. All of these data will be transmitted to the application via wireless sensor network (WSN) and other communications equipment, the network layer is responsible for handling this task. The data then received by the application layer is stored and passed to the applications that need the data in order to do their tasks which is the application layer. • The above-mentioned sensors measure the various environmental and weather-related parameters and monitor them constantly. The data from these sensors is constantly fed to an Arduino controller. The Arduino program checks for any irregularities in the sensor measurements and performs the associated computations. The Arduino also has a Wi-Fi module attached to it, which enables it to send the sensor data to the remote IoT platform using the IoT protocols over the Wi-Fi connection.
• The LCD is used to display the real-time values of the sensors. These data can also be viewed on the cloud, which constantly retrieves the information from the remote IoT platform. • If the value of any sensor crosses over a certain threshold value, an alert is sent to the end user via the wifi module. Using this system, the flood-related parameters can be monitored from anywhere in the world remotely. • In this system we make use of an Arduino with sensors to predict flood and alert respective authorities and sound instant alarm in nearby villages to instantly transmit information about possible floods using IoT. • All these features provided by the application can be efficiently used by any individual to monitor the system. It is user friendly and avoids complication of different data used as the user is only provided with what really is important.
REQUIREMENTS Hardware Arduino: The Arduino is the heart of the system all the sensors are connected to the Arduino and they operate in a synchronized manner. Wifi module: The ESP8266 is a System on a Chip (SoC), you get Wi-Fi communication, so you can use it to connect to your Wi-Fi network, or connect to cloud. Temperature and humidity sensor: DHT11 sensor for measuring temperature and humidity. LCD display: 2x16 for displaying the data. Ultrasonic sensor: HC-SR04 is used to measure the distance from the sensor to the water level. Connecting wires, Bread Board
REQUIREMENTS Software • Python • Django • HTML and CSS • WampServer
ALGORITHM • Random forest, the "forest" it builds consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction. (It can be used for both classification and regression tasks) • Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. • The reason that the random forest model works so well is: A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models. • The larger the number of trees, the more accurate the result. RANDOM FOREST
FLOOD PPT 1.pptx
UML Diagrams • Use Case Diagram
UML Diagrams Class Diagram​
LITERATURE REVIEW Sr. No. Title of the Paper with Author Name Year Remark 1 K Vinothini, Dr. S. Jayanthy, “IoT Based Flood Detection and Notification System using Decision Tree Algorithm”, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019) IEEE Xplore Part Number: CFP19K34-ART; ISBN: 978-1-5386-8113-8. 2019 The design and implementation of flood detection and notification system based on Decision Tree algorithm is proposed in this work. The system divides the flood level into three stages for gathering data from sensors which is processed using PIC Microcontroller. 2 Minakshi Roy “Flood Detection and Water Monitoring System Using IOT”, International Journal Of Engineering And Computer Science · July 2020, DOI: 10.18535/ijecs/v9i07.4499. 2020 This paper has tried to propose a potential and economic solution to the problem of floods. Floods cannot be predicted easily, but we are trying to develop a system which tries to detect flood and give early intimation to nearby people. 3 Shahirah Binti Zahir1, Phaklen Ehkan2, Thennarasan Sabapathy3, Muzammil Jusoh4, Mohd Nasrun Osman5, Mohd Najib Yasin6, Yasmin Abdul Wahab7 N.A.M Hambali8, N.Ali9, A.S.Bakhit10,F.Husin11, M.K.Md.Kamil12 R.Jamaludin13, “Smart IoT flood Monitoring System”, International Conference Computer Science and Engineering Journal of Physics: Conference Series 1339 (2019) 012043 IOP Publishing doi:10.1088/1742-6596/1339/1/012043. 2019 Nowadays the Internet Of things (IoT) is broadly used in worldwide, this system will display the data of the water level measured on web server. If there is continuous heavy rain, user can simply monitor the water level through laptop or mobile phone wherever they are as long there is an internet connection. 4 Hung Ngoc Do, Minh-Thanh Vo, Van-Su Tran, Phuoc Vo Tan, and Cuong Viet Trinh, “An Early Flood Detection System Using Mobile Networks”, 2015 International Conference on Advanced Technologies for Communications (ATC), 978-1-4673- 8374-5/15/$31.00 ©2015 IEEE. 2015 A low-cost early flood detection system has been designed and implemented. This system has high mobility characteristic due to the compact size of control board and solar energy usage, so it can be installed easily. The flood warning is divided into three levels depending on the measured data from sensors. For each warning level, the system will update the duration of getting data and sending result via SMS and Internet. 5 Garima Singh, Nishita Bisht, Pravesh Bisht, Prajjwal Singh, “Iot Based Flood Monitoring and Alerting System with Weather Forecasting”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020, Retrieval Number: F3854049620/2020©BEIESP DOI: 10.35940/ijitee.F3854.049620 2020 This project highlights the possibility to provide an alert system that will overcome the risk of flood. It can also contribute to multiple government agencies or authority that can ultimately help the society and mankind about the flood like hazardous natural disaster.
5 GARIMA SINGH, NISHITA BISHT, PRAVESH BISHT, PRAJJWAL SINGH, “IOT BASED FLOOD MONITORING AND ALERTING SYSTEM WITH WEATHER FORECASTING”, INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND EXPLORING ENGINEERING (IJITEE) ISSN: 2278-3075, VOLUME-9 ISSUE-6, APRIL 2020, RETRIEVAL NUMBER: F3854049620/2020©BEIESP DOI: 10.35940/IJITEE.F3854.049620 2020 THIS PROJECT HIGHLIGHTS THE POSSIBILITY TO PROVIDE AN ALERT SYSTEM THAT WILL OVERCOME THE RISK OF FLOOD. IT CAN ALSO CONTRIBUTE TO MULTIPLE GOVERNMENT AGENCIES OR AUTHORITY THAT CAN ULTIMATELY HELP THE SOCIETY AND MANKIND ABOUT THE FLOOD LIKE HAZARDOUS NATURAL DISASTER. 6 Zayar Soe, Phyo Zaw, “IoT Based Flood Detection System”, https://www.researchgate.net/publication/333293876, May2019,DOI: 10.13140/RG.2.2.16198.73281. 2019 We are creating a system that is highly reliable and worth investing. • Our team will manage installation and maintenance • We are looking forward to building a safe and developed future together. 7 Neha Suresh1, Ipsita Behera2, Payal Bhagat3, Payel Thakur4, “EARLY FLOOD MONITORING SYSTEM USING IOT APPLICATIONS”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 2020 This project based on the Early Flood Monitoring using IOT to detect & monitor the water level. In this project we are using Raspberry pi, LED, Buzzer, ultrasonic sensor, Android Application. 8 Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Paul Fergus, Ibrahim Olatunji Idowu, “Advance Flood Detection and Notification System based on Sensor Technology and Machine Learning Algorithm”, 978-1-4673-8353-0/15/$31.00 ©2015 IEEE. 2015 Flood detection system has been designed for immediate notification to the local authorities. It determined the current water level using sensor network, which provides notification via SMS and web base public network through GSM modem. SMS and web base public network are valuable alert communication tools that can distribute the information to the flood’s victims within particular area. 9 Bilal Arshad 1,* , Robert Ogie 1 , Johan Barthelemy 1 , Biswajeet Pradhan 2,3 , Nicolas Verstaevel 4 and Pascal Perez 1, “Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review”, Received: 4 October 2019; Accepted: 12 November 2019; Published: 16 November 2019, Sensors 2019, 19, 5012; doi:10.3390/s19225012 www.mdpi.com/journal/sensors. 2019 This paper presented a systematic review of the literature regarding computer vision and IoT-based sensors for flood monitoring and mapping. The review found that there are a wide range of applications that support computer vision techniques and the IoT-based sensor approach for improved monitoring and mapping of floods. 10 Dolly Kumaria, Leena Mahatob, Golden Kumarc, Goutam Kumard, Kumar Abhinab e, Jaydeep Kumarf, Pradip Acharjeeg, Arijit Duttah, “Study on IoT based Early flood detection and avoidance”, International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC 2020), https://ssrn.com/abstract=3652362 2020 Nowadays the Internet Of things (IoT) is broadly used in worldwide, this system will display the data of the water level measured on web server. • If there is continuous heavy rain, user can simply monitor the water level through laptop or mobile phone wherever they are as long there is an internet connection.
REFERENCES • [1] K Vinothini, Dr. S. Jayanthy, “IoT Based Flood Detection and Notification System using Decision Tree Algorithm”, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019) IEEE Xplore Part Number: CFP19K34-ART; ISBN: 978-1-5386-8113-8. • [2] Minakshi Roy “Flood Detection and Water Monitoring System Using IOT”, International Journal Of Engineering And Computer Science · July 2020, DOI: 10.18535/ijecs/v9i07.4499. • [3] Shahirah Binti Zahir1, Phaklen Ehkan2, Thennarasan Sabapathy3, Muzammil Jusoh4, Mohd Nasrun Osman5, Mohd Najib Yasin6, Yasmin Abdul Wahab7 N.A.M Hambali8, N.Ali9, A.S.Bakhit10,F.Husin11, M.K.Md.Kamil12 R.Jamaludin13, “Smart IoT flood Monitoring System”, International Conference Computer Science and Engineering Journal of Physics: Conference Series 1339 (2019) 012043 IOP Publishing doi:10.1088/1742-6596/1339/1/012043. • [4] Hung Ngoc Do, Minh-Thanh Vo, Van-Su Tran, Phuoc Vo Tan, and Cuong Viet Trinh, “An Early Flood Detection System Using Mobile Networks”, 2015 International Conference on Advanced Technologies for Communications (ATC), 978-1-4673-8374-5/15/$31.00 ©2015 IEEE. • [5] Garima Singh, Nishita Bisht, Pravesh Bisht, Prajjwal Singh, “Iot Based Flood Monitoring and Alerting System with Weather Forecasting”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020, Retrieval Number: F3854049620/2020©BEIESP DOI: 10.35940/ijitee.F3854.049620 • [6] Zayar Soe, Phyo Zaw, “IoT Based Flood Detection System”, https://www.researchgate.net/publication/333293876, May 2019, DOI: 10.13140/RG.2.2.16198.73281. • [7] Neha Suresh1, Ipsita Behera2, Payal Bhagat3, Payel Thakur4, “EARLY FLOOD MONITORING SYSTEM USING IOT APPLICATIONS”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072. • [8] Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Paul Fergus, Ibrahim Olatunji Idowu, “Advance Flood Detection and Notification System based on Sensor Technology and Machine Learning Algorithm”, 978-1-4673-8353-0/15/$31.00 ©2015 IEEE. • [9] Bilal Arshad 1,* , Robert Ogie 1 , Johan Barthelemy 1 , Biswajeet Pradhan 2,3 , Nicolas Verstaevel 4 and Pascal Perez 1, “Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review”, Received: 4 October 2019; Accepted: 12 November 2019; Published: 16 November 2019, Sensors 2019, 19, 5012; doi:10.3390/s19225012 www.mdpi.com/journal/sensors. • [10] Dolly Kumaria, Leena Mahatob, Golden Kumarc, Goutam Kumard, Kumar Abhinab e, Jaydeep Kumarf, Pradip Acharjeeg, Arijit Duttah, “Study on IoT based Early flood detection and avoidance”, International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC 2020), https://ssrn.com/abstract=3652362
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FLOOD PPT 1.pptx

  • 1. Advanced Flood Detection System based on Sensor Technology and Machine Learning Algorithm Guided By: Prof. Kanchan Mahajan Sandip Foundation's​ Sandip Institute of Technology and Research Centre, Nashik Department of Computer Engineering​ Presented By: • Akshay Jadhav [B150614244]​​ • Kshitija Khadke [B150614256]​ • Shraddha Gite [B150614238]​
  • 2. OUTLINE • INTRODUCTION • OBJECTIVES • PROBLEM STATEMENT • SCOPE OF PROJECT • ARCHITECTURE • REQUIREMENTS • ALGORITHM • UML DIAGRAMS • LITERATURE REVIEW • REFERENCES
  • 3. INTRODUCTION • Floods are a major catastrophic event that allows for the catastrophic destruction of any nation, affecting human lives and making outrageous harm to people and properties. • Without proper monitoring and effective mitigation measures, these natural perils often culminate in disasters that have severe implications in terms of economic loss, social disruptions, and damage to the urban environment. • The aim is to build an efficient flood warning system while maintaining reasonable production cost has been a meaningful mission for many researchers • In this system, a cluster of servers will collect and process data from the hydrological observation stations in real time and with the help of Random Forest Algorithm for the classification of data the available results can be displayed on client computer by remote access and issue warnings if there is a risk of flood with the help of IoT.
  • 4. OBJECTIVE  The main objective of this project is to develop and design a flood detection system that will detect flood automatically and transmit data through IoT.  In the first stage, system will detect the current water levels on flood by taking sensor values from outside environment and it will give real-time information to the appropriate station about severity.  At the second stage, the machine learning algorithm Random Forest is executed for classification and examine the level of flood, information to evaluate if the level of water is typical or in unsafe condition with the help of threshold values.  The proposed system is a low cost in design and easy for maintenance. This project will update the water level at the web server and the system will issue an alert signal to the citizens for evacuation so that fast and necessary action can be taken.
  • 5. PROBLEM STATEMENT • Many flood warnings stations have been developed and installed in prosperous countries but the manufacturing cost is usually too high to be practical in developing countries. • Therefore, building an efficient flood warning system while maintaining reasonable production cost has been a meaningful mission for many researchers including our project.
  • 6. SCOPE OF THE PROJECT • Historical records have shown that flood is the most frequent natural hazard, accounting for 41% of all-natural perils that occurred globally in the last decade. In this period alone (2009 to 2019), there were over 1566 flood occurrences affecting 0.754 billion people around the world with 51,002 deaths recorded and damage estimated at $371.8 billion. Put in context, these statistics only account for “reported” cases of large-scale floods. • The ultimate goal was to improve the prediction accuracy, for this purpose some researchers have explored the correlation among weather features and prediction accuracy and tried to find the best combinations of those features to tune the performance. • Few researchers on the other hand worked to train the mining technique well to achieve the high accuracy in prediction. Few have compared the modern techniques with the conventional ones. • Nevertheless, the current situation calls for improved ways of monitoring and responding to floods. The importance of improved flood monitoring cannot be overemphasized given the growing uncertainty associated with climate change and the increasing numbers of people living in flood-prone areas.
  • 8. • To detect a flood the system observes various natural factors, which includes humidity, temperature, water level and flow level. To collect data of mentioned natural factors the system consists of different sensors which collects data for individual parameters. • For detecting changes in humidity and temperature the system has a DHT11 Digital Temperature Humidity Sensor and for the water level measurement it has water level sensor WL400. • The system has a wi-fi connectivity, using the ESP8266 Wifi module, which connects the system to cloud; thus, it’s collected data can be accessed from anywhere quite easily using IoT. • All these sensors are then connected to ARDUINO UNO, which processes and saves data. • The data movement from sensors to application can be viewed as a series of layered architecture that consists of perception layer, network layer and application layer. • The perception layer consists of various devices involved in getting the geological data. All of these data will be transmitted to the application via wireless sensor network (WSN) and other communications equipment, the network layer is responsible for handling this task. The data then received by the application layer is stored and passed to the applications that need the data in order to do their tasks which is the application layer. • The above-mentioned sensors measure the various environmental and weather-related parameters and monitor them constantly. The data from these sensors is constantly fed to an Arduino controller. The Arduino program checks for any irregularities in the sensor measurements and performs the associated computations. The Arduino also has a Wi-Fi module attached to it, which enables it to send the sensor data to the remote IoT platform using the IoT protocols over the Wi-Fi connection.
  • 9. • The LCD is used to display the real-time values of the sensors. These data can also be viewed on the cloud, which constantly retrieves the information from the remote IoT platform. • If the value of any sensor crosses over a certain threshold value, an alert is sent to the end user via the wifi module. Using this system, the flood-related parameters can be monitored from anywhere in the world remotely. • In this system we make use of an Arduino with sensors to predict flood and alert respective authorities and sound instant alarm in nearby villages to instantly transmit information about possible floods using IoT. • All these features provided by the application can be efficiently used by any individual to monitor the system. It is user friendly and avoids complication of different data used as the user is only provided with what really is important.
  • 10. REQUIREMENTS Hardware Arduino: The Arduino is the heart of the system all the sensors are connected to the Arduino and they operate in a synchronized manner. Wifi module: The ESP8266 is a System on a Chip (SoC), you get Wi-Fi communication, so you can use it to connect to your Wi-Fi network, or connect to cloud. Temperature and humidity sensor: DHT11 sensor for measuring temperature and humidity. LCD display: 2x16 for displaying the data. Ultrasonic sensor: HC-SR04 is used to measure the distance from the sensor to the water level. Connecting wires, Bread Board
  • 11. REQUIREMENTS Software • Python • Django • HTML and CSS • WampServer
  • 12. ALGORITHM • Random forest, the "forest" it builds consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction. (It can be used for both classification and regression tasks) • Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. • The reason that the random forest model works so well is: A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models. • The larger the number of trees, the more accurate the result. RANDOM FOREST
  • 14. UML Diagrams • Use Case Diagram
  • 16. LITERATURE REVIEW Sr. No. Title of the Paper with Author Name Year Remark 1 K Vinothini, Dr. S. Jayanthy, “IoT Based Flood Detection and Notification System using Decision Tree Algorithm”, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019) IEEE Xplore Part Number: CFP19K34-ART; ISBN: 978-1-5386-8113-8. 2019 The design and implementation of flood detection and notification system based on Decision Tree algorithm is proposed in this work. The system divides the flood level into three stages for gathering data from sensors which is processed using PIC Microcontroller. 2 Minakshi Roy “Flood Detection and Water Monitoring System Using IOT”, International Journal Of Engineering And Computer Science · July 2020, DOI: 10.18535/ijecs/v9i07.4499. 2020 This paper has tried to propose a potential and economic solution to the problem of floods. Floods cannot be predicted easily, but we are trying to develop a system which tries to detect flood and give early intimation to nearby people. 3 Shahirah Binti Zahir1, Phaklen Ehkan2, Thennarasan Sabapathy3, Muzammil Jusoh4, Mohd Nasrun Osman5, Mohd Najib Yasin6, Yasmin Abdul Wahab7 N.A.M Hambali8, N.Ali9, A.S.Bakhit10,F.Husin11, M.K.Md.Kamil12 R.Jamaludin13, “Smart IoT flood Monitoring System”, International Conference Computer Science and Engineering Journal of Physics: Conference Series 1339 (2019) 012043 IOP Publishing doi:10.1088/1742-6596/1339/1/012043. 2019 Nowadays the Internet Of things (IoT) is broadly used in worldwide, this system will display the data of the water level measured on web server. If there is continuous heavy rain, user can simply monitor the water level through laptop or mobile phone wherever they are as long there is an internet connection. 4 Hung Ngoc Do, Minh-Thanh Vo, Van-Su Tran, Phuoc Vo Tan, and Cuong Viet Trinh, “An Early Flood Detection System Using Mobile Networks”, 2015 International Conference on Advanced Technologies for Communications (ATC), 978-1-4673- 8374-5/15/$31.00 ©2015 IEEE. 2015 A low-cost early flood detection system has been designed and implemented. This system has high mobility characteristic due to the compact size of control board and solar energy usage, so it can be installed easily. The flood warning is divided into three levels depending on the measured data from sensors. For each warning level, the system will update the duration of getting data and sending result via SMS and Internet. 5 Garima Singh, Nishita Bisht, Pravesh Bisht, Prajjwal Singh, “Iot Based Flood Monitoring and Alerting System with Weather Forecasting”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020, Retrieval Number: F3854049620/2020©BEIESP DOI: 10.35940/ijitee.F3854.049620 2020 This project highlights the possibility to provide an alert system that will overcome the risk of flood. It can also contribute to multiple government agencies or authority that can ultimately help the society and mankind about the flood like hazardous natural disaster.
  • 17. 5 GARIMA SINGH, NISHITA BISHT, PRAVESH BISHT, PRAJJWAL SINGH, “IOT BASED FLOOD MONITORING AND ALERTING SYSTEM WITH WEATHER FORECASTING”, INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND EXPLORING ENGINEERING (IJITEE) ISSN: 2278-3075, VOLUME-9 ISSUE-6, APRIL 2020, RETRIEVAL NUMBER: F3854049620/2020©BEIESP DOI: 10.35940/IJITEE.F3854.049620 2020 THIS PROJECT HIGHLIGHTS THE POSSIBILITY TO PROVIDE AN ALERT SYSTEM THAT WILL OVERCOME THE RISK OF FLOOD. IT CAN ALSO CONTRIBUTE TO MULTIPLE GOVERNMENT AGENCIES OR AUTHORITY THAT CAN ULTIMATELY HELP THE SOCIETY AND MANKIND ABOUT THE FLOOD LIKE HAZARDOUS NATURAL DISASTER. 6 Zayar Soe, Phyo Zaw, “IoT Based Flood Detection System”, https://www.researchgate.net/publication/333293876, May2019,DOI: 10.13140/RG.2.2.16198.73281. 2019 We are creating a system that is highly reliable and worth investing. • Our team will manage installation and maintenance • We are looking forward to building a safe and developed future together. 7 Neha Suresh1, Ipsita Behera2, Payal Bhagat3, Payel Thakur4, “EARLY FLOOD MONITORING SYSTEM USING IOT APPLICATIONS”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 2020 This project based on the Early Flood Monitoring using IOT to detect & monitor the water level. In this project we are using Raspberry pi, LED, Buzzer, ultrasonic sensor, Android Application. 8 Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Paul Fergus, Ibrahim Olatunji Idowu, “Advance Flood Detection and Notification System based on Sensor Technology and Machine Learning Algorithm”, 978-1-4673-8353-0/15/$31.00 ©2015 IEEE. 2015 Flood detection system has been designed for immediate notification to the local authorities. It determined the current water level using sensor network, which provides notification via SMS and web base public network through GSM modem. SMS and web base public network are valuable alert communication tools that can distribute the information to the flood’s victims within particular area. 9 Bilal Arshad 1,* , Robert Ogie 1 , Johan Barthelemy 1 , Biswajeet Pradhan 2,3 , Nicolas Verstaevel 4 and Pascal Perez 1, “Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review”, Received: 4 October 2019; Accepted: 12 November 2019; Published: 16 November 2019, Sensors 2019, 19, 5012; doi:10.3390/s19225012 www.mdpi.com/journal/sensors. 2019 This paper presented a systematic review of the literature regarding computer vision and IoT-based sensors for flood monitoring and mapping. The review found that there are a wide range of applications that support computer vision techniques and the IoT-based sensor approach for improved monitoring and mapping of floods. 10 Dolly Kumaria, Leena Mahatob, Golden Kumarc, Goutam Kumard, Kumar Abhinab e, Jaydeep Kumarf, Pradip Acharjeeg, Arijit Duttah, “Study on IoT based Early flood detection and avoidance”, International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC 2020), https://ssrn.com/abstract=3652362 2020 Nowadays the Internet Of things (IoT) is broadly used in worldwide, this system will display the data of the water level measured on web server. • If there is continuous heavy rain, user can simply monitor the water level through laptop or mobile phone wherever they are as long there is an internet connection.
  • 18. REFERENCES • [1] K Vinothini, Dr. S. Jayanthy, “IoT Based Flood Detection and Notification System using Decision Tree Algorithm”, Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019) IEEE Xplore Part Number: CFP19K34-ART; ISBN: 978-1-5386-8113-8. • [2] Minakshi Roy “Flood Detection and Water Monitoring System Using IOT”, International Journal Of Engineering And Computer Science · July 2020, DOI: 10.18535/ijecs/v9i07.4499. • [3] Shahirah Binti Zahir1, Phaklen Ehkan2, Thennarasan Sabapathy3, Muzammil Jusoh4, Mohd Nasrun Osman5, Mohd Najib Yasin6, Yasmin Abdul Wahab7 N.A.M Hambali8, N.Ali9, A.S.Bakhit10,F.Husin11, M.K.Md.Kamil12 R.Jamaludin13, “Smart IoT flood Monitoring System”, International Conference Computer Science and Engineering Journal of Physics: Conference Series 1339 (2019) 012043 IOP Publishing doi:10.1088/1742-6596/1339/1/012043. • [4] Hung Ngoc Do, Minh-Thanh Vo, Van-Su Tran, Phuoc Vo Tan, and Cuong Viet Trinh, “An Early Flood Detection System Using Mobile Networks”, 2015 International Conference on Advanced Technologies for Communications (ATC), 978-1-4673-8374-5/15/$31.00 ©2015 IEEE. • [5] Garima Singh, Nishita Bisht, Pravesh Bisht, Prajjwal Singh, “Iot Based Flood Monitoring and Alerting System with Weather Forecasting”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-6, April 2020, Retrieval Number: F3854049620/2020©BEIESP DOI: 10.35940/ijitee.F3854.049620 • [6] Zayar Soe, Phyo Zaw, “IoT Based Flood Detection System”, https://www.researchgate.net/publication/333293876, May 2019, DOI: 10.13140/RG.2.2.16198.73281. • [7] Neha Suresh1, Ipsita Behera2, Payal Bhagat3, Payel Thakur4, “EARLY FLOOD MONITORING SYSTEM USING IOT APPLICATIONS”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072. • [8] Mohammed Khalaf, Abir Jaafar Hussain, Dhiya Al-Jumeily, Paul Fergus, Ibrahim Olatunji Idowu, “Advance Flood Detection and Notification System based on Sensor Technology and Machine Learning Algorithm”, 978-1-4673-8353-0/15/$31.00 ©2015 IEEE. • [9] Bilal Arshad 1,* , Robert Ogie 1 , Johan Barthelemy 1 , Biswajeet Pradhan 2,3 , Nicolas Verstaevel 4 and Pascal Perez 1, “Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review”, Received: 4 October 2019; Accepted: 12 November 2019; Published: 16 November 2019, Sensors 2019, 19, 5012; doi:10.3390/s19225012 www.mdpi.com/journal/sensors. • [10] Dolly Kumaria, Leena Mahatob, Golden Kumarc, Goutam Kumard, Kumar Abhinab e, Jaydeep Kumarf, Pradip Acharjeeg, Arijit Duttah, “Study on IoT based Early flood detection and avoidance”, International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC 2020), https://ssrn.com/abstract=3652362