DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020 Flood Prediction System Using IoT and Artificial Neural Networks with Edge Computing Eric Samikwa KTH ROYAL INSTITUTE OF TECHNOLOGY ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Flood Prediction System Using IoT and Artificial Neural Networks with Edge Computing Eric Samikwa 2020-06-18 Master’s Thesis Place for Project: RISE Research Institutes of Sweden Examiner: Magnus Boman Supervisors at RISE: Thiemo Voigt and Joakim Eriksson Supervisor at KTH: Ying Liu ii Abstract Flood disasters affect millions of people across the world by causing severe loss of life and colossal damage to property. Internet of things (IoT) has been applied in areas such as flood prediction, flood monitoring, flood detection, etc. Although IoT technologies cannot stop the occurrence of flood disasters, they are exceptionally valuable apparatus for conveyance of catastrophe readiness and counteractive action data. Advances have been made in flood prediction using artificial neural networks (ANN). Despite the various advancements in flood prediction systems through the use of ANN, there has been less focus on the utilisation of edge computing for improved efficiency and reliability of such systems. Inthis thesis, a system for short-term flood prediction that uses IoT and ANN, where the prediction computation is carried out on a low power edge device is proposed. The system monitors real-time rainfall and water level sensor data and predicts ahead of time flood water levels using long short-term memory. The system can be deployed on battery power as it uses low power IoT devices and communication technology. The results of evaluating a prototype of the system indicate a good performance in terms of flood prediction accuracy and response time. The application of ANN with edge computing will help improve the efficiency of real-time flood early warning systems by bringing the prediction computation close to where data is collected. Keywords Internet of things; Flood prediction; Artificial neural network; Edge computing; Long short-term memory. iii Abstract Översvämningar drabbar miljontals människor över hela världen genom att orsaka dödsfall och förstöra egendom. Sakernas Internet (IoT) har använts i områden som översvämnings förutsägelse, översvämnings övervakning, översvämning upptäckt, etc. Även om IoT-teknologier inte kan stoppa förekomsten av översvämningar, så är de mycket användbara när det kommer till transport av katastrofberedskap och motverkande handlingsdata. Utveckling har skett när det kommer till att förutspå översvämningar med hjälp av artificiella neuronnät (ANN). Trots de olika framstegen inom system för att förutspå översvämningar genom ANN, så har det varit mindre fokus på användningen av edge computing vilket skulle kunna förbättra effektivitet och tillförlitlighet. I detta examensarbete föreslås ett system för kortsiktig översvämningsförutsägelse genom IoT och ANN, där gissningsberäkningen utförs över en låg effekt edge enhet. Systemet övervakar sensordata från regn och vattennivå i realtid och förutspår översvämningsvattennivåer i förtid genom att använda långt korttidsminne. Systemet kan köras på batteri eftersom det använder låg effekt IoT-enheter och kommunikationsteknik. Resultaten från en utvärdering av en prototyp av systemet indikerar en bra prestanda när det kommer till noggrannhet att förutspå översvämningar och responstid. Användningen av ANN med edge computing kommer att förbättra effektiviteten av tidiga varningssystem för översvämningar i realtid genom att ta gissningsberäkningen närmare till där datan samlas. Nyckelord Sakernas Internet; Översvämning av översvämningar; Artificiella neuronnät; Kant datoranvändning; Långt kortvarigt minne iv Acknowledgements I would like to express my gratitude to the supervisors Thiemo Voigt and Joakim Eriksson at Research Institutes of Sweden for their guidance and support during my thesis work. I would like to thank my thesis examiner Magnus Boman and thesis supervisor Ying Liu at KTH for their invaluable help during the finalization of my thesis. Finally, I would like to extend my appreciation to Swedish Institute for granting me scholarship to study in Sweden. v Acronyms and Abbreviations IoT Internet of Things BLE Bluetooth Low Energy WSN Wireless Sensor networks RNN Recurrent Neural Network LSTM Long Short Term Memory ANN Artificial Neural Network RMSE Root Mean Squared Error NARX Nonlinear Autoregressive network with EXogenous inputs R the correlation coefficient MAE Mean Absolute Error ReLU Rectified Linear Unit MSE Mean Squared Error Adam Adaptive moment estimation FFNN Feed Forward Neural Network SGD Stochastic Gradient Descent vi Contents 1 Introduction 1 1.1 Problem Statement ..................... 2 1.2 Objectives .......................... 3 1.3 Scope and Limitations .................... 3 1.4 Research Methodology .................... 4 1.5 Structure of the Report ................... 4 2 Background 5 2.1 Internet of Things ...................... 5 2.2 Edge Computing ....................... 6 2.2.1 Edge Computing Platforms ............. 7 2.3 Recurrent Neural Networks ................. 8 2.4 Flood Forecasting Using ANN ................ 11 2.5 Related Work ........................ 12 3 Design and Implementation 15 3.1 Proposed System ...................... 15 3.2 IoT Framework ........................ 16 3.2.1 IoT Wireless Technologies Alternatives ....... 17 3.3 ANN Analysis ........................ 19 3.3.1 Dataset Description ................. 19 3.3.2 Data Preparation .................. 20 3.3.3 Short-Term Flood Prediction Using LSTM ..... 21 3.3.4 Alternative Methods for Flood Prediction ...... 24 3.4 System Flow ......................... 25 3.5 System Setup ........................ 26 3.5.1 TensorFlow Lite ................... 26 3.5.2 Raspberry Pi 3B+ .................. 27 3.5.3 Arduino Nano 33 BLE ................ 28 vii CONTENTS 3.5.4 Sensors ........................ 28 4 Evaluation 30 4.1 Forecast Accuracy ...................... 30 4.2 Performance of the Prototype ................ 36 4.2.1 Response Time ................... 36 4.3 Comparison of Alternative Design Approaches ....... 37 4.3.1 Model Size Reduction by Tensorflow Lite ...... 38 4.3.2 Performance Comparison of ReLU, Sigmoid and Tanh Activation Functions ................. 38 4.3.3 Performance Comparison of Adam and SGD Optimizers 39 5 Conclusions and Future Work 40 5.1 Conclusions ......................... 40 5.2 Future Work ......................... 41 References 42 Appendix 48 A Prototype Demo 48 A.1 Sensor Data Visualisation .................. 48 A.2 Flood Alert in the Form of ThingTweet ........... 49 viii Chapter 1 Introduction Floods are among the most common damaging natural disasters that affect millions of people across the world leading to severe loss oflifeand colossal damage to property, infrastructure and agriculture. According to the World Meteorological Organization, flooding remains the third biggest disaster in the world [1]. Due to climate change, scientists estimate a 4-inch sea level rise by 2030, which could potentially cause severe flooding in many parts of the world[2]. Based on a research conducted by Institute of Environmental Studies, more that 60% of world cities will be vulnerable to flooding in the next 30 years due to effects of the sea level rise [3]. Studies have been conducted in different areas such as flood data collection, flood prediction, flood monitoring, flood detection, flood early warning systems, and flood data visualization, with an aim of reducing the impact of flood disasters by alerting the affected societies abouta flood occurrence ahead of time. With current technological advancements in the domains of sensing systems, wireless communication networks, cloud computing, machine learning, and data science, it is possible to develop an integrated flood disaster management system which can efficiently alert the flood affecting regions. Internet of Things (IoT) is a core technology being used in flood early warning systems. IoT characteristics provide effective guarantee for ahead of time perception and precaution, advance to reduce the impact of disasters [4]. Despite the fact that IoT technologies cannot stop the occurrence of disasters, they are exceptionally valuable apparatus for conveyance of catastrophe readiness and counteractive action data. Such data can be used for geographical flood simulation modeling5 [ ], which 1 CHAPTER 1. INTRODUCTION aids in policy making in flood disaster risk management. For real time flood early warning systems, information delivery is key [6]. Thus, there is a need to ensure that information delivery must be concise, right to the point, useable and in timely manner. There are several factors that are attributed to the efficiency and effectiveness of early warning systems for floods. These include the correctness of prediction of a flood occurrence, the amount of time needed to makea prediction, the reliability of the communication networks used in the early warning system, the deployment and maintenance cost of the systems, etc. This thesis presents a system for real time flood prediction that utilises IoT sensing and artificial neural networks where processing of the sensor data is carried out on a low power edge device. 1.1 Problem Statement One major challenge for the future of IoT applications is that all data collected from end devices, such as IoT sensors has to be sent via the Internet to a central processing platform.