Smart Weather Alert System for dwellers of different Areas

Durrani, Areeha; Khurram, Muhammad; Khan, Hashim Raza

doi:10.1109/ibcast.2019.8667190

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…MEIoT connects to the Internet, records eight meteorological factors, and uploads the data to the cloud. Using a mixture of IoT and machine learning, Durrani et al (2019) proposed a smart weather station that predicts and monitors meteorological data. Additionally, it generates quick warnings for residents of various places to alert them of coming hazards.…”

Section: Related Researchmentioning

confidence: 99%

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Fahim

Mhouti

Boudaa

et al. 2023

Model. Earth Syst. Environ.

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The automatic weather system serves to inform farmers, tourists, planners, and others with precise information to help them take the appropriate action. Today, with the advancement of smart technologies, the system has evolved into many sensing methods to gather real-time climate data. This article investigates the modeling and implementation of a low-cost weather station device that also functions to measure air quality. The proposed system based on the Internet of Things (IoT) allows access to real-time climate data for a given area. This system monitors environmental conditions such as ambient temperature, humidity, atmospheric pressure, altitude, and levels of harmful atmospheric gases like CO 2 and NO 2 . This real-time telemetry device uses MQ-135, DHT-11 and BMP280 sensors to gather data. The ESP32 board processes the obtained data from all sensors. Additionally, we present a model for a fuzzy inference system (FIS) that performs parameter categorization using a reasoning procedure and incorporates the results into an air quality index (AQI) that describes the levels of pollution for Al Hoceima city. The FIS takes CO 2 and NO 2 values as input and returns the AQI. The AQI for Al Hoceima city is categorized into six levels: Excellent , Good , Regular , Bad , Dangerous , and Very Dangerous . Furthermore, the suggested system's block hardware employs the Message Queuing Telemetry Transport (MQTT) protocol to broadcast collected data to a mobile and web application via the Internet. The suggested IoT-embedded device was tested in real life, and the results were promising.

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Section: Related Researchmentioning

confidence: 99%

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Fahim

Mhouti

Boudaa

et al. 2023

Model. Earth Syst. Environ.

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“…In 2019, Durrani et al [20] proposed a smart weather station for monitoring weather parameters. This system is equipped with various sensors that collect data from their location and then send them to the cloud.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-based data logger for weather monitoring using arduino-based wireless sensor networks with remote graphical application and alerts

Mabrouki

Azrour²,

Dhiba

et al. 2021

Big Data Min. Anal.

109

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In recent years, the monitoring systems play significant roles in our life. So, in this paper, we propose an automatic weather monitoring system that allows having dynamic and real-time climate data of a given area. The proposed system is based on the internet of things technology and embedded system. The system also includes electronic devices, sensors, and wireless technology. The main objective of this system is sensing the climate parameters, such as temperature, humidity, and existence of some gases, based on the sensors. The captured values can then be sent to remote applications or databases. Afterwards, the stored data can be visualized in graphics and tables form.

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“…As a result, the system includes a variety of sensors employed with Raspberry Pi card. Durrani, Khurram & Khan (2019) developed a smart weather station. Their design is outfitted with a number of environmental sensors that communicates with the cloud.…”

Section: Introductionmentioning

confidence: 99%

Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees

ALTINBİLEK

Nar

Aksu

et al. 2022

Journal of Advanced Research in Natural and Applied Sciences

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Meteorology stations sold in the market have various difficulties in terms of their use, also these systems are costly to obtain. With state of the art sensor technologies, the development of mini weather stations has become easier. This study focuses on the development of a model weather station device using temperature, relative humidity, UV, LDR Light, rain and soil moisture sensors to collect major environmental data. The measured data were wirelessly transmitted to the remote station for logging via the GSM module and the information was sent to the database in the internet environment. In addition, the data from the sensors are organized by correlation. The classification was made according to the data obtained from the rain sensor and the relationship between the other 5 sensors used in the device to the rain classification was examined. Sensor data were scaled between 0-1 with min-max normalization before being subjected to deep learning and machine learning training. In the Decision Tree (DT) a model score of 0.96 was obtained by choosing the maximum depth of 20. The artificial neural network (ANN) yielded a classification score of 0.92 using 4 hidden layers and 100 epochs in the artificial neural network model.

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Smart Weather Alert System for dwellers of different Areas

Cited by 8 publications

References 7 publications

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

Modeling and implementation of a low-cost IoT-smart weather monitoring station and air quality assessment based on fuzzy inference model and MQTT protocol

IoT-based data logger for weather monitoring using arduino-based wireless sensor networks with remote graphical application and alerts

Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees

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