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.

/pdf/iot-based-data-logger-for-weather-monitoring-using-arduino-2xp4la0423.pdf

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

Guidance for the Verification and Validation of Neural Networks: Pullum/Guidance for the Verification and Validation of Neural Networks

Stakeholder perspectives on extreme hot and cold weather alerts in England and the proposed move towards an impact-based approach

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 CO2 and NO2. 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 CO2 and NO2 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. 

/pdf/modeling-and-implementation-of-a-low-cost-iot-smart-weather-1jgmrqq6.pdf

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

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. 

/pdf/sensory-precipitation-forecast-using-artificial-neural-rurnngq1.pdf

Sensory Precipitation Forecast Using Artificial Neural Networks and Decision Trees

With changing climate, heatstroke has proved to be disastrous for few countries especially. The dwellers of different areas are not warned of the consequences to come specifically in their areas as they are told the average of the whole city, while temperature varies at different altitudes and over short distances. The solution provided in the paper, is a smart weather station that not only monitor weather data but also predict it and generate instant alerts for dwellers of different areas, to help them be warned of the future hazard, using the combination of Internet of things and Machine Learning. It is deployed with different sensors that collect weather data from the environment, which are sent to cloud, where predictions are made, for which certain neural network models have been compared to find out which gives the most accurate results. Those values, as well as the real-time values can be displayed on the mobile Application 24/7. Also, alerts are generated in the form of Tweets, which are accessible to everyone, as shown in Figure-1. It is also discovered that Nonlinear Autoregressive Exogenous Neural Network (NARXNET) Algorithm is the best to be implemented for prediction of Weather, with the mean squared error of 0.084% in 1.55 seconds for training a model and producing predictions for next 24 hours.

H. R Khan

Papers

Smart Weather Alert System for dwellers of different Areas