Weather Monitoring Stations Research Articles

Development of an early warning system to reduce the impact of floods related to glacial lake outburst floods (SAGAZ)

Abstract. The retreat of glaciers has led to increasing in natural hazards due, for example, by emptying of lakes dammed by unconsolidated glacial deposits (moraines) or ice, which are susceptible to catastrophic erosion, generating rapid floods, phenomena known as “Glacial Lake Outburst Floods” (GLOFs). Current systems that warn of the occurrence of a GLOF are activated when the emptying begins, which leaves little time to act, so they should be considered early alarm systems. The challenge is to predict the onset of the flood allowing the generation of an early warning system. To address this problem, SAGAZ ultimately aims to develop a system capable of identifying periods of increased GLOF risk using a predictive model fed by weather forecasts and monitoring station data. This system identifies a period of higher risk, which allows informing authorities several days in advance. This paper presents the results of the first phase of SAGAZ implementation, which aimed to (1) develop and validate a prototype monitoring station and deploy a network of stations on glacial lakes across southern Patagonia, (2) collect the necessary data for the development, training and validation of predictive models and (3) begin the implementation and testing of the predictive model. As a result, a network of 10 monitoring stations was installed in the Aysén and Magallanes regions of Chile and 1 in the Province of Santa Cruz in Argentina, of which 6 are currently operational and transmitting data in real time. The rest went off due to power failures and icebergs damaging sensors. The measures we have taken to avoid station’s failures are described, as well as some characteristics of the implemented prototype, the installed networks and the data obtained so far.

Performance evaluation of waste phosphogypsum-based solidified sludge: From laboratory test to field application

Massive dredged sludge is being landfilled without effective use due to its high-water content and poor engineering properties, which not only leads to soil resources waste, but also occupies a large amounts of land sources. In this study, ternary stabilizer, including waste phosphogypsum (PG), ground granulated blast-furnace slag (GGBS), and lime (LM) with a mixing proportion of PG: GGBS: LM = 35:60:5, was adopted to improve the mechanical and environmental behaviors of sludge for subgrade filling purpose. The initial water content of sludge was controlled using two different dehydration methods for comparison. A series of laboratory tests, including unconfined compressive strength (UCS), organic matter content, and pH value were tested to understand its physical-mechanical properties. Thereafter, field application model equipped with a mini weather monitoring station was constructed to monitor the influence of solidified matrix on the surrounding water and soil environment. Time -dependent parameters such as plant growth, temperature, humidity, total nitrogen, phosphorus/potassium content, electrical conductivity, and pH value were monitored. Results indicate that the incorporation of PG-GGBS-LM ternary stabilizer significantly improves the mechanical and environmental properties of dredged sludge. The optimal dosage of the ternary stabilizer is 36%, which can result in a UCS value of the 2.0 MPa (slightly higher than ordinary Portland cement) after 28 days of curing. Field application reveals that plants could grow normally in solidified sludge. The environmental related parameters (i.e., total nitrogen, phosphorus/potassium content, electrical conductivity, and pH value) were similar with those in conventional planting soil, suggesting the advantage of the proposed PG-GGBS-LM ternary stabilizer in mechanical, economic and environmental aspects.

WSN-Based Data Acquisition System for Collecting Environmental Pollution Factors for Green City

Implementation with a portable, intelligent weather station is to upgrade the system for environmental management and monitoring. This gadget measures and assesses meteorological and air quality data by real-time implementation through the Internet of Things technology to provide important environmental condition insights. The monitoring station's hardware parts include the ESP32 board, MQ2, MQ3, and MQ135 sensors for gas detection. Such an intelligent weather monitoring station can easily be deployed in different environments due to its portability and versatility. This small station size and its wireless link will enable effective data collection and transfer and integration into other systems of the present day. The existing environmental monitoring system takes the temperature and humidity and measures ammonia, benzene, alcohol, smoke, and carbon monoxide. Therefore, data is brought to the monitoring station, where it communicates in real time for the users to review and analyse on the online platform Blynk. It enables users to easily make data presentations and create notifications and dashboards using a mobile app of Blynk and the web platform, enabling them to create interfaces that meet their satisfaction easily. Users may also upload the uploaded data to a web server for remote access and analysis, which eases scalability and flexibility. Portable intelligent weather monitoring station has significant areas of influence on applications like public health, urban planning, and environmental monitoring, among others. It thus provides decision-makers with information on pollution management and climate resilience. This provides a sustainable and resilient future; it is approachable since the interface used in the system is accessible, befitting the two settings: urban and rural. The Intelligent Portable Weather Monitoring Station provides great usefulness in the disclosure, development, innovation of technology, and practice in the quest for meeting challenges of the environment with the promotion of sustainability. This not only allows smarter and more sustainable city development with effective environmental monitoring and management that includes IoT technologies but can also allow flexible data transmission facilities.

Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared (R2) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin.

Tropospheric Ozone levels in and around southern city Bengaluru, India

The diurnal to seasonal ozone (O3) pre-cursors (NO, NO2, CO, VOCs) trends over local climatic zones (LCZs) with the changes in weather patterns with respect to land cover classes determines the O3 trends and useful for air quality management. In this work, diurnal to seasonal patterns of O3 concentrations are analysed across the different LCZs in Bengaluru region. The Central/State Pollution Control Boards (CPCB/SPCB) operate more than 500 continuous air quality and weather monitoring stations across the India. Hourly O3 concentration data were collected from CPCB for 5 different LCZs within the Bengaluru region over a period of four years (2019-2022). Time series analyses were conducted to examine the temporal variations in O3 levels. The results showed significant fluctuations in diurnal trends of O3 due to the influence of air temperature and relative humidity variations across the five LCZs within Bengaluru region. There were distinct seasonal trends in O3 concentrations with maximum (60-65 ppb) observed during the winter and summer seasons and that of minimum concentration in the range 20-25 ppb during the monsoon months. The observed maximum ozone levels (60 -65 ppb) found to be beyond the threshold national ambient air quality standard (NAAQ) and the accumulation exposure over threshold of 40 ppb (AOT 40) during the winter and summer. The O3 concentration trends are in line with the published research on premature mortality about 1200 annually for Karnataka state for chronic obstructive pulmonary disease owing to population exposure to O3 beyond its threshold level of NAAQ. Also, the annual rice yield loss is about 2% (~0.1 Metric Tonne) with the AOT 40 to paddy crop for Karnataka state. Thus, O3 diurnal to seasonal trends serve the first-hand information to local authorities for the action plan of air quality management taking into consideration O3 precursors, wind speed, and wind direction.

Increasing duration of heatwaves poses a threat to oyster sustainability in the Gulf of Mexico

The future of the wild oyster fishery in the northern Gulf of Mexico is largely uncertain due to changing environmental conditions and declining abundance of harvestable oysters. Specifically, rising temperatures can directly impact the physiological thresholds of the eastern oyster (Crassostrea virginica) at all life history stages and alter the narrow ecological niche this oyster occupies. The impact of rising temperatures is likely most pronounced during atmospheric heatwaves, defined as three or more days above the 90th percentile of daily maximum air temperatures, which have been shown to be increasing in frequency. Increasing exposure to high temperature extremes may contribute to and exacerbate an already declining oyster fishery. Critical to fishery health is recruitment i.e., the addition of new harvestable biomass, which is a dynamic process strongly driven by temperature. Here, we examine the relationship between heatwave characteristics and the prediction of poor oyster recruitment, measured as the abundance of post-larval oysters (e.g. spat) below the site-specific median density observed in historically productive oyster fisheries over 46-years (1976 – 2020) in Mobile Bay, Alabama and 21-years (1993 – 2014) in Apalachicola Bay, Florida. We acquired daily maximum air temperature measurements measured over 50 years (1970 – 2020) at weather monitoring stations adjacent to the bays to identify site specific annual heatwave events (maximum yearly air temperature, yearly and consecutive heatwave days, and number of annual heatwaves). Then, years with extreme heatwaves that exceeded the 75th percentile for the 50-year measurements were compared to years with non-extreme heatwave events. Years with extreme total heatwave days and extreme consecutive heatwave days were correlated with low post-larval oyster density. Across both bay systems, if consecutive heatwave days exceeded 11 days, then poor recruitment of oysters occurred 83 % of the time. Extreme heatwave duration as an indicator for poor recruitment has the potential to be a powerful tool for fishery managers to forecast recruitment and inform sustainable oyster harvest based on year-to-year variability in heatwave duration and long-term warming trends. Our findings illustrate how extreme temperatures can exacerbate multiple physiological and ecological stressors resulting in the loss of a keystone species for healthy and resilient coastal ecosystems.

Exposure to ambient temperature and heat index in relation to DNA methylation age: A population-based study in Taiwan

BackgroundClimate change caused an increase in ambient temperature in the past decades. Exposure to high ambient temperature could result in biological aging, but relevant studies in a warm environment were lacking. We aimed to study the exposure effects of ambient temperature and heat index (HI) in relation to age acceleration in Taiwan, a subtropical island in Asia. MethodsThe study included 2,084 participants from Taiwan Biobank. Daily temperature and relative humidity data were collected from weather monitoring stations. Individual residential exposure was estimated by ordinary kriging. Moving averages of ambient temperature and HI from 1 to 180 days prior to enrollment were calculated to estimate the exposure effects in multiple time periods. Age acceleration was defined as the difference between DNA methylation age and chronological age. DNA methylation age was calculated by the Horvath’s, Hannum’s, Weidner’s, ELOVL2, FHL2, phenotypic (Pheno), Skin & blood, and GrimAge2 (Grim2) DNA methylation age algorithms. Multivariable linear regression models, generalized additive models (GAMs), and distributed lag non-linear models (DLNMs) were conducted to estimate the effects of ambient temperature and HI exposures in relation to age acceleration. ResultsExposure to high ambient temperature and HI were associated with increased age acceleration, and the associations were stronger in prolonged exposure. The heat stress days with maximum HI in caution (80-90°F), extreme caution (90-103°F), danger (103-124°F), and extreme danger (>124°F) were also associated with increased age acceleration, especially in the extreme danger days. Each extreme danger day was associated with 571.38 (95 % CI: 42.63–1100.13), 528.02 (95 % CI: 36.16–1019.87), 43.9 (95 % CI: 0.28–87.52), 16.82 (95 % CI: 2.36–31.28) and 15.52 (95 % CI: 2.17–28.88) days increase in the Horvath’s, Hannum’s, Weidner’s, Pheno, and Skin & blood age acceleration, respectively. ConclusionHigh ambient temperature and HI may accelerate biological aging.

Impact of Temperature and Atmospheric Pressure on Hospitalizations of Patients Presenting With Acute Coronary Syndrome.

This study aims to investigatethe impact of temperature and atmospheric pressure on hospitalizations of patients with acute coronary syndrome (ACS). This is a retrospective, observational, analytical study conducted in a single center, University Hospital Center "Mother Teresa," Tirana, Albania, in the period January-December 2018. This study included 1,165 patients with ACS, who performed urgent coronary angiography, from January 2018 to December 2018. Patients were diagnosed withACS based on clinical and examination findings. The data were collected retrospectively using patient files. Baseline demographic, clinical, and procedural characteristics were collected. Data on atmospheric parameters, measured at the weather monitoring station, were obtained from the National Meteorological Service database. Measurements from the meteorological service provided values ​​for each parameter: average daily temperature and atmospheric pressure in each country district. Atmospheric data measurements were taken for the day under review. The number of inhabitants for the respective districts is taken from the National Institute of Statistics (INSTAT). The study involved 1,165 patients, with a mean age of 63.1 years, ranging from 27 years to 89 years old. The majority of patients (78.6%) were male, while 21.4% were female. A statistically significant relationship was observed between seasonal changes in temperature and atmospheric pressure concerning the number of cases with ACS; the autumn season prevails with 27.9% of the total cases, followed by the spring season with 25.6%, the summer season with 24.2%, and winter season with 22.3% (p = 0.04). Additionally, significant changes in the average monthly values ​​of temperature and atmospheric pressure were accompanied by a statistically significant increase in the number of cases as occurred in March-April and October-November (p ≤ 0.05). Most cases in the cold period (November-March) occurred on days with a change in temperature or atmospheric pressure with a statistically significant value of p < 0.05. An important relationship between seasonal, monthly, and daily changes in temperature and atmospheric pressure concerning the frequency of cases with ACS was observed.

Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

Kill Two Birds With One Stone: A Multi-View Multi-Adversarial Learning Approach for Joint Air Quality and Weather Prediction

Accurate and timely air quality and weather predictions are of great importance to urban governance and human livelihood. Though many efforts have been made for air quality or weather prediction, most of them simply employ one another as feature input, which ignores the inner-connection between two predictive tasks. On one hand, the accurate prediction of one task can help improve another task's performance. On the other hand, geospatially distributed air quality and weather monitoring stations provide additional hints for city-wide spatiotemporal dependency modeling. Inspired by the above two insights, in this paper, we propose a multi-view multi-adversarial approach, entitled MasterGNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> , to jointly predict air quality and weather conditions. First, we devise a multi-view graph learning block to model spatial autocorrelation based on geographical distance and environmental context. Then, a dedicated evolved recurrent network is proposed to dynamically capture the long-range and independent temporal autocorrelation for each monitoring station and time slot. After that, we develop a multi-adversarial graph learning framework to against observation noise propagation introduced by spatiotemporal modeling. Moreover, we present an adaptive training strategy by formulating multi-adversarial learning as a multi-task learning problem. Finally, extensive experiments on two real-world datasets show that MasterGNN <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> achieves the best performance compared with seven baselines on both air quality and weather prediction tasks.

Simulations of winter ozone in the Upper Green River basin, Wyoming, using WRF-Chem

Abstract. In the Upper Green River basin (UGRB) of Wyoming and the Uintah Basin of Utah, strong wintertime ozone (O3) formation episodes leading to O3 mixing ratios occasionally exceeding 70 parts per billion (ppb) have been observed over the last 2 decades. Wintertime O3 events in the UGRB were first observed in 2005 and since then have continued to be observed intermittently when meteorological conditions are favorable, despite significant efforts to reduce emissions from oil and natural gas extraction and production. While O3 formation has been successfully simulated using observed volatile organic compound (VOC) and nitrogen oxide (NOx) mixing ratios, successful simulation of these wintertime episodes using emission inventories in a 3-D photochemical model has remained elusive. An accurate 3-D photochemical model driven by an emission inventory is critical to understanding the spatial extent of high-O3 events and which emission sources have the most impact on O3 formation. In the winter of 2016/17 (December 2016–March 2017) several high-O3 events were observed with 1 h mixing ratios exceeding 70 ppb. This study uses the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate one of the high-O3 events observed in the UGRB during March 2017. The WRF-Chem simulations were carried out using the 2014 edition of the Environmental Protection Agency National Emissions Inventory (EPA NEI2014v2), which, unlike previous versions, includes estimates of emissions from non-point oil and gas production sources. Simulations were carried out with two different chemical mechanisms: the Model for Ozone and Related Chemical Tracers (MOZART) and the Regional Atmospheric Chemistry Mechanism (RACM), and the results were compared with data from seven weather and air quality monitoring stations in the UGRB operated by the Wyoming Department of Environmental Quality (WYDEQ). The simulated meteorology compared favorably to observations with regard to temperature inversions, surface temperature, and wind speeds. Notably, because of snow cover present in the basin, the photolysis surface albedo had to be modified to predict O3 in excess of 70 ppb, although the models were relatively insensitive to the exact photolysis albedo if it was over 0.65. O3 precursors, i.e., NOx and VOCs, are predicted similarly in simulations with both chemical mechanisms, but simulated VOC mixing ratios are a factor of 6 or more lower than the observations, while NOx is also underpredicted but to a lesser degree. Sensitivity simulations revealed that increasing NOx and VOC emissions to match observations produced slightly more O3 compared to baseline simulations, but an additional sensitivity simulation with doubled NOx emissions resulted in a considerable increase in O3 formation. These results suggest that O3 formation in the basin is most sensitive to NOx emissions.

Design, Simulation and Construction of a Digital Mobile Weather Monitoring Station

Despite the importance of weather in aviation industry, agriculture practices and other sectors, the measurement of atmospheric parameters in real time monitoring has been sparsely discussed in the literature and the importance of digital mobile weather station has not been well stressed out. In this study, design and construction of a digital mobile weather station was carried out using Arduino Nano microcontroller ATMega385p. The circuit was simulated using Proteus v8.7. The constructed device was used to measure temperature, humidity and sunlight. The device readings were compared with standard measured values obtained from Nigerian Metrological Agency (NIMET) in the month of February and August. Result shows that the slope of the regression line (zero intercept) relating device measurement to standard readings from NIMET in the month of February for temperature was 1.01 (R 2 = 0.923) while that of humidity was 1.006 (R 2 = 0.987), and sunlight was 1.002 (R 2 = 0.999). Also, the slope of the regression line (zero intercept) relating device measurement to standard readings from NIMET in the month of August for temperature was 0.994 (R 2 = 0.985) while that of humidity was 1.002 (R 2 = 1), and sunlight was 0.998 (R 2 = 0.991). This implies that there is no significant difference between device measurement and standard readings from NIMET since both regression lines accounts for ? 98.23% of the variance. However, the device can be used to obtain data on weather parameters with great degree of accuracy for flight monitoring, surfing, plant growth and development, and marine technology.

Development of an alternative device for measurement and characterization of selected meteorological parameters

Weather monitoring and forecasting during some of nature’s most violent events, such as lightning and thunder, necessitates immediate preventive action for improved agricultural precision, power equipment effectiveness among others. Weather stations that are all-in-one for villages, low-income communities, and cities could provide a dependable, cost-effective, robust, and user-friendly solution. A wide range of low-cost weather monitoring stations equipped with ground-based and satellite-based lightning detectors are available on the market. This paper develops a low-cost real-time data logger device that measures lightning strikes and other weather parameters. Temperature and relative humidity are detected and recorded by the sensor (BME280). The sensing unit, readout circuit unit, microcontroller unit, recording unit, real-time clock, display unit, and power supply unit are the seven sections of the lightning detector with a real-time data logger. The sensing unit of the instrument is made of a lightning sensor glued to a polyvinyl chloride (PVC) to prevent moisture inflow and short circuit. The readout circuit consists of a 16-bit analog-to-digital converter and a filter designed to improve the output signal of the lightning detector. It was programmed with C-language and tested using the integrated development environment on the Arduino-Uno microcontroller (IDE). The device was calibrated, and its accuracy was determined using data from a standard lightning detector instrument from the Nigerian Meteorological Agency (NIMET).

Representativeness of Energy Rating acc. to IEC 61853 for Different Locations in Middle and South Europe

The energy rating standard series IEC 61 853 parts 1–4 provide a procedure to calculate the performance of a photovoltaic (PV) module for six different reference climates. The results, denoted as climate specific energy rating (CSER), aim to predict the module's energy output for a complete year under maximum power point operation. The reference climate profiles shall represent the typical operating conditions of a PV module in a given region. Our study investigates the representativeness of these reference climates profiles for several locations in South and Central Europe. We gathered both ground‐ and satellite‐based data sets from different weather monitoring stations. Using such data for our specific location in Freiburg, Germany, and a specific reference module, we found deviations ranging from 2.0% to 2.9% compared to the CSER using the temperate continental profile. Further, we analyze the annual impact of the loss factors: reflectivity as function of angle of incidence, solar spectrum, ambient temperature, and irradiance on module performance. For an in‐depth PV module benchmarking, we recommend the use of site‐specific meteorological data for an accurate module performance assessment, rather than relying on the more general estimates from the standard procedure, which do not intend to capture site‐specific effects.

Cost Effective Weather Monitoring Station

The system suggested in this study is a cutting-edge approach for tracking meteorological conditions in a specific location and making the data available elsewhere in the globe. The Internet of Items (IoT) is the technology underlying this, and it is a sophisticated and efficient method for connecting things to the internet and linking the entire universe of things in a network. Electronic devices, sensors, and vehicle electronic equipment might all be found here. The system uses sensors to monitor and regulate ambient factors such as temperature and relative humidity, and then sends the data to a web page, where it is plotted as graphical statistics. The data updated by the established system may be accessed over the internet from any location on the planet.

Wind farm site suitability assessment & validation using geospatially explicit multi-criteria approach: A case study of South Sikkim, India

ABSTRACT Determination of optimal location for wind farm is a challenging task influenced by several decision criteria and factors subject to human. This case study done proposes an integrated decision support framework to investigate the spatial wind farm suitability based on F-AHP (Fuzzy-Analytical Hierarchy Process) together with Q-GIS (Quantum-Geographic Information System) tool. Firstly, spatial analysis and integration of nine-exclusion factors were included to identify the unsuitable sites. Next, F-AHP-based MCDM (Multi Criteria Decision Making) analysis was employed to obtain criteria weights of seven evaluation factors. Unlike traditional AHP method, the proposed F-AHP method is much flexible to deal with intermediate linguistic variable. F-AHP results reveal that wind speed is the most important criteria followed by power Tr. lines with weighted value of 33.05% and 20.95% respectively. The thematic suitability map was developed on Q-GIS platform to classify from “least suitable” to “most suitable” classes. The final suitable map indicates 26.09 km2 and 138.42 km2 area are most suitable and highly suitable respectively for wind farm establishment. This study is also supported with ground measurement wind resource data collected through a WMS (Weather Monitoring Station) installed at “most suitable” region of suitability indexed class.

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

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. Thisarticleinvestigatesthemodelingandimplementationofalow-cost weatherstationdevicethatalsofunctions to measureairquality. 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.

IOT based Weather Sensing and Monitoring Station

IOT based Weather Sensing and Monitoring Station

Rice Growth Stage Classification via RF-Based Machine Learning and Image Processing

Rice is one of the most significant crops cultivated in Asian countries. In Taiwan, almost half of the arable land is used for growing rice. The life cycle of paddy rice can be divided into several stages: vegetative stage, reproductive stage, and ripening stage. These three main stages can be divided into more detailed stages. However, the transitions between stages are challenging to observe and determine, so experience is required. Thus, rice cultivation is challenging for inexperienced growers, even with the standard of procedure (SOP) provided. Additionally, aging and labor issues have had an impact on agriculture. Furthermore, smart farming has been growing rapidly in recent years and has improved agriculture in many ways. To lower the entry requirements and help novices better understand, we proposed a random forest (RF)-based machine learning (ML) classification model for rice growth stages. The experimental setup installed in the experiment fields consists of an HD smart camera (Speed-dome) to collect the image and video data, along with other internet of things (IoT) devices such as 7-in-1 soil sensors, a weather monitoring station, flow meter, and milometer connected with LoRa base station for numerical data. Then, different image processing techniques such as object detection, object classification, instance segmentation, excess green index (EGI), and modified excess green index (EGI) were used to calculate the paddy height and canopy cover (CC) or green coverage (GC). The proposed ML model uses these values as input. Furthermore, growth-related factors such as height, CC, accumulative temperature, and DAT are used to develop our model. An agronomist has been consulted to label the collected different stages of data. The developed optimal model has achieved an accuracy of 0.98772, and a macro F1-score of 0.98653. Thus, the developed model produces high-performance accuracy and can be employed in real-world scenarios.

An empirical study of the key factors affecting herders' purchasing decision on weather index insurance—A case study from inner Mongolia autonomous region, China

The weather index insurance for mutton sheep (WIMS) is the first weather index insurance product that addresses the increased feeding cost-related risk of grassland animal husbandry herders in China. In 2019, the WIMS was implemented as a pilot project in 11 county regions of Xilin Gol League, Inner Mongolia, China. Since the implementation of this product, the purchasing rate has varied from region to region, as the highest purchasing rate was 84.3%, while the lowest was only 1.8%. This research uses the double hurdle model to identify the factors affecting herders' purchasing behavior. This paper reports a survey study of 535 participants herders in the Xilin Gol League. The result indicates that herders' age, disaster risk evaluation, insurance awareness, pasture area, and neighborhood purchasing decision significantly impact herders' purchasing propensity. Disaster evaluation, insurance awareness, pasture area and location significantly impact the purchasing degree of herders. The study suggests that young herders and herders with large pasture areas should be motivated to purchase insurance. In addition, new methods should be applied for the publicity of this insurance product; Investments should be focused on the automatic weather monitoring station projects in pastoral areas to reduce the basis risk of the insurance.