[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124744-en":3,"doc-seo-124744-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124744,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Design and Implementation of Machine Learning Models and Algorithms for Flood, Drought and Frazil Prediction","Natural calamities like floods and droughts threaten human safety and impose substantial economic losses. This thesis develops advanced machine learning methods to improve water-height prediction accuracy for flood mitigation. It evaluates an LSTM-based architecture across three scenarios—frazil, drought, and flood conditions from snowmelt—while incorporating meteorological forecasts as an input parameter. The study compares next-hour, next-day, and next-week meteorological data to assess forecast quality impact on predictions. It further integrates IoT sensor data with an online machine learning training approach for adaptive, real-time flood prediction.","DESIGN AND IMPLEMENTATION OF MACHINE LEARNING MODELS AND ALGORITHMS FOR FLOOD, DROUGHT AND  \nFRAZIL PREDICTION  \nBHARGAV YAGNIK  \nA THESIS  \nIN  \nTHE DEPARTMENT  \nOF  \nCOMPUTER SCIENCE AND SOFTWARE ENGINEERING  \nPRESENTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF COMPUTER SCIENCE  \nCONCORDIA UNIVERSITY  \nMONTRAL, QUBEC, CANADA  \nAUGUST 2023  \n© BHARGAV YAGNIK, 2023  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Bhargav Yagnik  \nEntitled: Design and Implementation of Machine Learning Models and Al  \ngorithms for Flood, Drought and Frazil Prediction  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Computer Science  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the final examining committee:  \n  Chair  \nDr. BentalebAbdelhak  \n  Examiner  \nDr. Sudhir Mudur  \n  Supervisor  \nDr. Brigitte Jaumard  \n  Co-supervisor Dr. Tristan Glatard  \nApproved by    \nDr. Leila Kosseim, Graduate Program Director  \n  2023    \nDr. Mourad Debbabi, Dean  \nGina Cody School of Engineering and Computer Science  \nAbstract  \nDesign and Implementation of Machine Learning Models and Algorithms for  \nFlood, Drought and Frazil Prediction  \nBhargav Yagnik  \nNatural calamities like floods and droughts pose a significant threat to humanity, impacting millions of people each year and incurring substantial economic losses to society. In response to this challenge, this thesis focuses on developing advanced machine learning techniques to improve water height prediction accuracy that can aid municipalities in effective flood mitigation.  \nThe primary objective of this study is to evaluate an innovative architecture that leverages Long Short Term Networks-neural networks to predict water height accurately in three different environmental scenarios, i.e., frazil, droughts and floods due to snow spring melt. A distinguishing feature of our approach is the incorporation of meteorological forecast as an input parameter into the prediction model. By modeling the intricate relationships between water level data, historical meteorological data and meteorological forecasts, we seek to evaluate the impact of meteorological forecasts and if any inaccuracies could impact water-level prediction. We compare the outcomes obtained by incorporating next-hour, next-day and next-week meteorological data into our novel LSTM model. Our results indicate a comprehensive comparison of the usage of various parameters as input and our findings suggest that accurate weather forecasts are crucial in achieving reliable water height predictions.  \nAdditionally, this study focuses on the utilization of IoT sensor data in combination with ML models to enhance the effectiveness of flood prediction and management. We present an online machine learning approach that performs online training of the model using real-time data from IoT sensors. The integration of live sensor data provides a dynamic and adaptive system that demonstrates superior predictive capabilities compared to traditional static models. By adopting  \nthese advanced techniques, we can mitigate the adverse impacts of natural catastrophes and work towards building more resilient and disaster-resistant communities.  \nKeywords: LSTM, Time-series forecasting, online machine learning, flood prediction.  \nAcknowledgments  \nI want to express my utmost gratitude to my supervisor Dr. Brigitte Jaumard and my co-supervisor Dr. Tristan Glatard for their constant support and guidance throughout my project. Their expertise, valuable insights and feedbacks have been instrumental in shaping the direction of my research and enhancing the quality of my work.  \nFurthermore, I thank Jean Michel Sellier, my project guide at Ericsson for his unwavering motivation and guidance throughout my project. I extend my sincere gratitude to Emmanuel Thepie Fa","cbCaisGxbUZvvpwW","https://ap.wps.com/l/cbCaisGxbUZvvpwW","pdf",1280018,1,64,"English","en",105,"# Contents\n## 1 Introduction\n### 1.1 General Background and Motivation\n### 1.2 Key references\n### 1.3 Contributions of the Thesis\n### 1.4 Organization of the Thesis\n## 2 ML Methods for Flood Prediction\n### 2.1 Introduction\n### 2.2 Background and Problem Statement\n#### 2.2.1 Problem Statement: Meteorological Event Predictions\n#### 2.2.2 Water Data\n#### 2.2.3 Meteorological Data\n#### 2.2.4 LSTM Machine Learning Models\n#### 2.2.5 Performance Evaluation","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To develop machine learning models that improve water-height prediction accuracy to support effective flood mitigation.\"},{\"question\":\"How does the thesis improve prediction beyond historical data?\",\"answer\":\"It incorporates meteorological forecast information as an input to the LSTM model and evaluates how forecast horizons affect results.\"},{\"question\":\"How are IoT sensors used in the proposed approach?\",\"answer\":\"IoT sensor data is combined with an online machine learning workflow that trains the model in real time, enabling adaptive predictions compared with static models.\"}]","Design and Implementation of Machine Learning Models and Algorithms for Flood, Drought and Frazil Prediction | 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