Department of Computer Technologies, Istanbul Aydin University, Istanbul 34295, Turkey.
Department of Computer Engineering, Istanbul Topkapi University, Istanbul 34087, Turkey.
Sensors (Basel). 2023 Feb 22;23(5):2426. doi: 10.3390/s23052426.
Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.
工业化和快速城市化几乎影响到每个国家的许多环境价值，如核心生态系统、区域气候差异和全球多样性。我们因快速变化而遇到的困难，使我们在日常生活中遇到了许多问题。这些问题的背景是快速数字化和缺乏足够的基础设施来处理和分析大量数据。物联网检测层生成的不准确、不完整或不相关的数据导致天气预报报告偏离准确性和可靠性的概念，因此基于天气预报的活动被打乱。天气预报是一项复杂而困难的工作，需要对大量数据进行观测和处理。此外，快速城市化、气候剧变和大规模数字化使得预测更加准确和可靠变得更加困难。不断增加的数据密度以及快速的城市化和数字化，使得预测更加准确和可靠变得更加困难。这种情况使人们无法对城市和农村地区的恶劣天气条件采取预防措施，从而将其转化为一个至关重要的问题。在这项研究中，提出了一种智能异常检测方法，以最小化由于快速城市化和大规模数字化而导致的天气预报问题。所提出的解决方案涵盖了物联网边缘的数据处理，包括从传感器获得的数据中过滤掉缺失、不必要或异常数据，以防止预测更加准确和可靠。在研究中还比较了五种不同机器学习（ML）算法的异常检测指标，包括支持向量分类器（SVC）、自适应增强（Adaboost）、逻辑回归（LR）、朴素贝叶斯（NB）和随机森林（RF）。这些算法被用于使用时间、温度、压力、湿度和其他传感器生成的信息创建数据流。
