Towards An Autonomous Radiation Early Warning System Mohammed Al Saleh Lebanese Atomic Energy Commission (LAEC) National Council For Scientific Research (CNRS) m.alsaleh@laec-cnrs.gov.lb B´ eatrice Finance, Yehia Taher David Laboratory University of Versailles (UVSQ) fname.lname@uvsq.fr Rafiqul Haque Intelligencia R&D Paris, France rafiqul.haque@intelligencia.fr Abstract—Although radiation level is a serious problem which requires continuous monitoring, many existing systems are de- signed to perform this task. Radiation Early Warning System (REWS) is one of these systems which monitor the gamma radiation level in the environment. On the other hand, such system requires high manual intervention, depends totally on experts analysis, and has some shortcomings that can be risky sometimes. In this paper we introduced our approach called RIMI (Refining Incoming Monitored Incidents) which aims to improve this system to become an autonomous system. We also introduced a new method to change this system to become a predictive and proactive system which learns from past incidents. Index Terms—Radiation, Early Warning System, Data Analyt- ics, Anomamy detection, etc. I. I NTRODUCTION Radiation level is one of the most critical hazards that must be taken care of due to its catastrophic and persistent consequences on the environment, humans and the other non- living things. Radioactive incidents and disasters such as Chernobyl [1], Fukushima [2], and the most recent one at Russian nuclear missile test site [3], raised a serious concern. These events have given rise to the need for continuous monitoring of the radiation level in the environment. Since the radiation can be transmitted through the wind, it is important to monitor the radioactivity within widespread geographical locations to prevent any unwanted exposure. The continuous monitoring would greatly help in taking a proactive measure that would eventually raise an alert upon an occurrence of incidence. Therefore, many countries around the world raised the idea of developing several techniques for monitoring the radiation level in the environment to detect any abnormal release or discharge. Lebanon was one of these countries that developed a national environmental radiation monitoring program to establish radiation baseline level and determine trend of radiation level in the country. Air monitoring was one of the scopes of this program.[Reference Public Exposure Article] There exist different approaches to monitor and analyze the impact of high radiation levels. Among them is the Radiation Early Warning System (REWS) that is a widely used network system which exists in Lebanon since 2013. The REWS is composed of many radiation detection sensors (also called probes) disseminated on a specific region that monitor the gamma radiation level. This system reacts as soon as possible to anomalies by raising an alert. Typically, the alerts are determined by predefined threshold values that are essentially chosen based on observations (i.e. experience). It is worth noting that there are different threshold values at different locations since the threshold value depends strictly on the normal reading of the radiation level (known as background level) which is in turn is not fixed due to many factors such as the altitude. Once an alert is raised, it needs to be checked by an expert. Indeed, the expert needs to analyze the potential causes for the incident as some alerts refer to an authentic threat of high radiation level and others denote the rise of radiation level that has no hazardous impact on the environment or living beings. In order to do so the expert will consult additional information such as the weather broadcast and the quality factors (also called quality bits) of the probe. For instance, the alert is false when the quality bits of the probe indicate that there is a defect in the probe, meaning that we cannot trust the collected gamma dose rate value. The alert is innocent when external factors have occurred such as rain, wind, lightening, etc. These external factors are the more difficult to analyse, but they represent more than 90% of the alarms. Finally, the alert is real and an emergency action need to be taken by the authority immediately. Existing REWS solutions have various shortcomings. The most critical one is the manual intervention of the expert that is heavily time-consuming, labor-intensive, and risk-prone. Indeed, when an alarm is raised a considerable amount of time and efforts are consumed by the expert to analyze the parameters that are stemming from external data sets such as weather data sets in order to classify the alert as false, innocent or real. As there is no automated data collector, the experts must carry out data searching and data fetching operations manually. Moreover, most of the time, the expert cannot classify the alert immediately as he/she needs to wait for further readings of the gamma dose rate to see if it will return to normal. This can take hours due to some parameters such as rain. Therefore, it is not possible to make a faster or real-time inference using the current approach. Today, we assist to the explosion of machine learning techniques and complex algorithms in order to help experts or 57 Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).