Index Of Agreement Research Articles

Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot

This study presents the design, simulation, and comparative analysis of three advanced control strategies applied to a 3-Degree-of-Freedom (DoF) robot manipulator. The controllers investigated are a variant from the Computed Torque Control family, a Proportional–Derivative–Integral with fuzzy logic (PD-PI + fuzzy) controller, and a Model Predictive Control (MPC) scheme. The controller performance is evaluated through the tracking of predefined trajectories in the three-dimensional space. The results are analyzed through XYZ coordinate motion graphs and 3D trajectories. To quantify performance, three error indicators are employed: Residual Mean Square (RMS) with a value of 0.0720 for the Computed Torque Controller, Residual Standard Deviation (RSD), and Index of Agreement (IA). The results demonstrate that the proposed controllers achieve accurate trajectory tracking, with IA values close to unity, demonstrating a high degree of concordance between the desired and executed trajectories.

A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines

Distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with batch processing machines (BPMs) is rarely considered, let alone DEHFSP with BPMs and uncertainty. In this study, a fuzzy DEHFSP with BPMs at a middle stage and no precedence between some stages is presented, and a dynamic artificial bee colony (DABC) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and fuzzy total energy consumption. To produce high quality solutions, Metropolis criterion is used, dynamic employed bee phase based on neighborhood structure dynamic selection is implemented, and group-based onlooker bee phase with bidirectional communication is given. Migration operator is also adopted to replace scout bee phase. Extensive experiments are conducted, and the optimal combination of key parameters for DABC is decided by the Taguchi method. Comparative results and statistical analysis show that new strategies of DABC are effective, and DABC is highly competitive in solving the considered fuzzy DEHFSP.

Embedded physical constraints in machine learning to enhance vegetation phenology prediction

ABSTRACT Vegetation phenology plays a pivotal role in ecological processes on terrestrial surfaces and the interactions between the biosphere and atmospheric feedback. Current attempts to retrieve vegetation phenology have primarily depended on vegetation indices extracted from satellite remote sensing imagery. These approaches often neglect the underlying physical mechanisms associated with climatic factors, and there is a notable absence of evaluations and comparisons with field-observed phenology inventory data. To address these limitations, this paper proposes an innovative physical constraint neural networks (PCNNs) model that combines machine learning techniques with physical mechanisms to enhance the accuracy of vegetation phenology predictions. By incorporating meteorological variables into a machine learning model and by using the Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset to identify the vegetation phenology of four vegetation types in North America, this study delved into the relationship between vegetation phenology and climate factors as well as its impacts on ecosystems. Our model demonstrated high accuracy compared to machine learning methods without physical mechanisms when validated by field observations from PhenoCam and the USA National Phenology Network (USA-NPN) spanning from 2001 to 2021. The results show that the overall root mean square error (RMSE) with physical constraints is reduced to 12.37 days, higher by 2.6 days than the machine learning method without physical constraints. We compared four vegetation types using different machine learning and traditional rule-based methods, deciduous vegetation (DV) exhibited the most favorable prediction results, with the RMSE and mean bias error (MBE) as low as 5.71 days and 4.06 days in PCNNs model, respectively. This was followed by evergreen needle-leaved forests and mixed forests with RMSE of 12.32 and 13.28 days, respectively. The stressed deciduous vegetation type had the worst prediction result of 19.86 days (RMSE), and the weighted index of agreement (WIA) attained a value of 0.68. These findings suggest that the model embedded with physical constraints significantly boosted prediction accuracy for four common vegetation types, particularly for DV, compared to the unconstrained ML model. It offers valuable insights into the incorporation of physical mechanisms within machine learning models. This research paves the way for substantial advancements in the field of land surface phenology, enabling more accurate and reliable predictions of vegetation phenology in various ecological and climatic contexts.

Predicting land cover changes in Xuan Lien Nature Reserve, Thanh Hoa province, Vietnam

The research was carried out in Xuan Lien Nature Reserve, which is a specially protected natural area and one of the five main centers of biodiversity in Vietnam. The work is aimed at analyzing changes and predicting the state of land cover in Xuan Lien Reserve. For these purposes, Landsat-7, Landsat-5 and Landsat-8 images obtained in 2000, 2010 and 2020 were studied. Normalized Difference Vegetation Index (NDVI) method was used to isolate the vegetation cover on the land areas, and Normalized Difference Water Index (NDWI) method was used. The results of the study indicate that the vegetation on the land is transforming in Xuan Lien Reserve. The area of broadleaf forests decreased by 6.2% from 2000 to 2020, and the area of wetlands increased by 4.9% due to the construction of Cua Dat hydroelectric complex in Reserve. The model of Markov chains and cellular automata was used to predict the state of the allocated lands in the study area. The predicted results of the model have been successfully confirmed using the agreement indices. According to the results of the predicted, by 2035 the area of broadleaf forests and wetlands will increase to 86.3% and 7.4%. The change in the structure of the vegetation cover of Reserve's land is explained by the measures taken by the Vietnamese government to regulate the water level in Cua Dat reservoir and expand the forest area.

A dynamical teaching-learning-based optimization algorithm for fuzzy energy-efficient parallel batch processing machines scheduling in fabric dyeing process

Fabric dyeing is the most time-consuming and energy-intensive process in textile production with some batch processing machines (BPMs) and uncertainty. In this study, a fuzzy energy-efficient parallel BPMs scheduling problem (FEPBSP) with machine eligibility and sequence-dependent setup time (SDST) in fabric dyeing process is investigated, and a dynamical teaching-learning-based optimization algorithm (DTLBO) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and total fuzzy energy consumption. In DTLBO, multiple classes are constructed by non-dominated sorting. Dynamical class evolution is designed, which incorporates diversified search among students and adaptive self-learning of teachers. The former is implemented using various combinations of the teacher phase and the learner phase, and the latter is achieved through teacher quality and an adaptive threshold. Additionally, a reinforcement local search based on neighborhood structure dynamic selection is also applied. Extensive experiments are conducted, and the computational results demonstrated that the new strategies of DTLBO are effective, and it is highly competitive in solving the considered problem.

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

A Delphi consensus project to capture experts' opinion on hyperkalaemia management across the cardiorenal spectrum.

The main purpose of this project was to capture experts' opinion on hyperkalaemia management and form best practice recommendations for cardiorenal patients in Greece. A steering committee of nephrologists and cardiologists developed 37 statements. An online questionnaire completed by 32 experts in cardiorenal management in Greece. Median score used to determine the level of agreement and disagreement index (DI) used to determine the level of consensus for each statement. Statements divided in four sectors: hyperkalaemia risk management, preventative measures, treatment and collaboration between specialties. The rate of the first round of the consensus was 94.6%. Median score was >7 for 36 of 37 statements and DI≤1 for 35 of 37. Among other statements, consensus reached for recognizing levels K+>5.0mEq/L as associated with elevated mortality risk; retaining renin-angiotensin-aldosterone system inhibitors (RAASi) on maximum recommended dose for cardiorenal patients; and using novel K+ binders to help enabling guideline-recommended doses of RAASi therapy. Cardiologists compared to nephrologists showed higher reluctance to discontinue down-titrate RAASi and MRA in patients with K+ levels above 5mEq/L. Additionally, 88.9% of nephrologists and 71.4% of cardiologists agreed that cross-specialty alignment on a serum K+ concentration level (K>5.5mEq/L) is needed to initiate hyperkalaemia treatment. Both cardiologists and nephrologists showed disagreement with the statement on keeping titration in cardiorenal patients with K+>5.5mEq/L or preserving fruit and vegetable consumption when moderate or severe hyperkalaemia exhibits. This Delphi project pointed out nephrologists' and cardiologists' agreement on hyperkalaemia management in cardiorenal patients; thus, it can help a cross-specialty optimal management of cardiorenal patients, with hyperkalaemia not being an obstacle for disease-optimizing therapy. Novel potassium binding agents can enable guideline-recommended doses of potassium-sparing medication.

Physical mechanism-based simulation methods for soil and ground heat flux from observations at multi-sites

It is important to obtain the soil heat flux at a specific depth (Gz) and at the ground’s surface (G0) to understand the surface energy balance and climate change. However, previous evaluations of Gz and G0 simulations based on multiple sites have been relatively insufficient. We evaluated the accuracy of using physical mechanism-based gradient and calorimetry methods to simulate Gz and changes in heat storage (ΔS) based on observations collected at 28 sites. Three methods for simulating G0 were also evaluated using triple collocation (TC) and extended triple collocation (ETC) metrics. The ability of the gradient method to simulate Gz was strong, particularly for the lower soil layer, with a median Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), and index of agreement for the evaluated sites of 0.76, 0.86, and 0.91, respectively. The accuracy of the calorimetry method for simulating ΔS was generally lower than that of the gradient method for determining Gz. Overall, the combination of gradient analysis and calorimetry showed the best performance in simulating G0. The TC evaluation showed that the combination of gradient and calorimetry had a strong ability to simulate G0 in most land cover types (root mean square error (RMSE) <4 W/m2) but this ability was lost in the snow cover type (RMSE > 10 W/m2). When evaluated by ETC, the three methods had strong effects only on alpine meadow, floodland, and shrub types (CC > 0.70). Appropriate evaluation indices were recommended based on the application of the model.

Agrometeorological modeling to estimate soybean productivity in the Southern Cone of Rondônia

Agrometeorological modeling is crucial to predict the impact of climatic conditions on agricultural production. However, its application in large regions faces challenges due to the lack of weather stations and limited access to real-time data. In this study, the multiplicative agrometeorological model was used to estimate soybean productivity in the seven municipalities of the Southern Cone of Rondônia in the 2020/21 to 2022/23 harvests. This model considers the relationships between actual and potential evapotranspirations and water conditions during different phenological stages. Ten-year water balances were developed for each municipality, using air temperature data from ERA5-Land from the ECMWF and precipitation data estimated by NASA’s GPM_v06 satellite. The results showed satisfactory performance of the model, with coefficients of determination ranging from 0.48 to 0.88 and agreement indices from 0.41 to 0.90 compared to the data observed by IBGE.

Evaluating the ability of the Wind Erosion Prediction System (WEPS) to simulate near-surface wind speeds in the Inland Pacific Northwest, USA

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS’s performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly.

A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM2.5) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a Depthwise-Partial Convolutional Neural Network (DW-PCNN) to fill gaps between surface PM2.5 stations, utilizing Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a Deep Convolutional Neural Network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM2.5 concentrations accurately, evidenced by a Pearson’s correlation coefficient (R) of 0.92 and an Index of Agreement (IOA) of 0.96 in ten-fold cross-validation. We also introduce a grid-based method for calculating PM2.5 Design Values (DV), providing a continuous spatial representation of PM2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model's ability to provide spatiotemporally consistent, gap-free PM2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.

Quantifying Solar Radiation Over Northern India and Validating with Cropwat for Agricultural and Renewable Energy Applications

Precise computation of global solar energy is crucial for many fields of investigation such as renewable energy, meteorology, and agriculture especially crop water requirement and irrigation requirement. This paper presents the quantification of solar radiation using weather data during the years from 2020-21. The results obtained were validated with the solar radiation estimated with the help of CROPWAT during the year. Statistical and visual approaches were adopted to validate the quantified results. Analysis revealed the high degree of similarity between simulated and CROPWAT determined daily solar radiation during the year 2020-21 over the study area. Very high values of Willmot index of agreement (1.0), Nash-Sutcliffe efficiency (0.999), and Pearson correlation coefficient (0.998), and low values of mean absolute error (0.138), mean bias error (-0.024), and root mean square error (0.182) between them was observed during the year 2020-21 in the area. The minimum solar radiation reaching over the region were observed to be around 5.71 MJ/m2/day during winter season (January) with a normal value of 13.50 MJ/m2/day, while the maximum value of 24.5 MJ/m2/day was observed during the summer season (June) with a normal value of 24.13 MJ/m2/day over the region. The use of these data for further study is recommended as it is worthful quantification over the region.

Developing some models to predict the uniaxial compressive strength of various sedimentary rocks (Case studies: Large dam site and mine in Southeast China)

Direct determination of uniaxial compressive strength (UCS) is time-consuming, expensive, and challenging. Therefore, this study aims to indirectly predict UCS using statistical and machine learning methods. First, laboratory measurements were conducted to determine carbonate percentage, water absorption percentage, moisture content, porosity percentage, P-wave velocity, and UCS of sandstone, dolomite, dolomitic limestone, marl limestone, and limestone samples. Subsequently, various models were established to predict UCS using multivariate linear regression (MVLR), random forest regression (RFR), Gaussian process regression (GPR) based on squared exponential kernel function, and adaptive neuro-fuzzy inference system (ANFIS). The influence of Jurassic limestone texture on physical and mechanical properties in stable and unstable slopes in the Three Gorges Dam site and reservoir (TGDSR) showed that the percentage of mudstone texture in unstable slopes is higher than in stable slopes. The UCS, density, compressional wave velocity, and percentage of carbonate in unstable slopes is less than stable slopes. The results indicated that the average measured UCS is 60.60 MPa. Moreover, the average predicted UCS based on the three intelligent methods was 60.55 MPa. The percentage difference between the measured and predicted UCS was −0.09 %, demonstrating that the average error of the three employed methods is less than one percent, highlighting the high accuracy of these methods in estimating rock UCS. Notably, based on error level, Taylor diagram, Wilmot agreement index (WAI), A10 and A20 indices, Nash-Sutcliffe efficiency (NSE), and PM (performance metric) criteria the RFR exhibited superior precision (R2=99 %, and RMSE= 0.06 MPa) compared to the other used methods. The Kruskal-Wallis test (KWT) results showed that there is no significant difference between the measured and predicted values.

B-087 Sars-cov-2 Nt Chip test performance: a novel microarray-based enzymatic immunoassay for in vitro identification of neutralizing antibodies against emerging variants

Abstract Background The COVID-19 pandemic has created a need for reliable and adaptable diagnostic tools to detect exposure and neutralizing antibodies to emerging SARS-CoV-2 variants. To address this need, we evaluated the performance of the SARS-CoV-2 NT Chip Test, an ELISA-like immunoblot assay that incorporates a customizable panel of purified antigens from the variants of interest. This assay detects antibodies that neutralize the interaction between the receptor-binding domain (RBD) of the Wuhan strain and the Omicron BA1 variant with the cellular ACE-2 receptor and provides a marker of prior infection through the nucleocapsid protein. Methods We analyzed 73 clinical samples from six distinct groups: GROUP 1: Pre-pandemic samples (33); GROUP 2: Samples from children naive to Wuhan and infected with the Omicron variant (10); GROUP 3: Samples from individuals infected only with the original Wuhan strain (10); GROUP 4: Samples from individuals vaccinated and/or infected with the Wuhan strain and the Omicron variant (10); GROUP 5: Samples collected from individuals with 0-2 days after Omicron BA.1 infection (5) and GROUP 6: Samples collected from the same individuals 10 days after Omicron BA.1 infection (5). Evaluation utilized a proprietary non-numerical (qualitative) validation spreadsheet. Performance metrics included sensitivity, specificity, observed agreement (≥90%), and kappa index (≥0.70). Results were compared against the manufacturer's validation using the PRNT viral neutralization assay. Results Our comparative evaluation showed the following results for each parameter: 1) Wuhan neutralizing antibodies (sensitivity and specificity of 92%, agreement of 92%, and kappa index of 0.84); 2) Omicron neutralizing antibodies (sensitivity and specificity of 100%, agreement of 100%, and kappa index of 1.00); 3) Anti-nucleocapsid antibodies (sensitivity of 97%, specificity of 98%, agreement of 97%, and kappa index of 0.94). Three samples from GROUP 1 showed positive results for Wuhan RBD and one for Nucleocapsid, all close to the cutoff value. Four samples from GROUP 2 showed positive results for Wuhan RBD, which may be attributed to maternal antibodies. Three samples from GROUP 3 and three samples from GROUP 4, showed negative results for nucleocapsid, agreeing with the manufacturer's validation. Samples from GROUP 5 and GROUP 6 showed expected results, with Omicron RDB negative in GROUP 5, becoming positive in GROUP 6. Conclusions The test showed excellent performance in the verification of the three parameters in the populations studied, being approved in our laboratory. Due to its ability to incorporate antigens from emerging variants, it enables sensitive tracking of changes in neutralizing antibody profiles over time. The automated and customizable nature of the test provides an important tool for both public health epidemiological studies, monitoring changes in humoral immunity as this virus evolves, and for individual diagnostics, revealing the level of humoral protection of a patient against circulating strains in a timely manner to guide care. Although further studies are still warranted, the assay's ability to map evolving strain-specific serological immunity may offer valuable insights.

Process‐Based Modeling of Ecosystem‐Level Monoterpene From a Japanese Larch (Larix kaempferi) Forest

AbstractGlobally, the emission of biogenic volatile organic compounds (BVOC) by plants represents the dominant source of volatile organic compounds emitted to the atmosphere. Monoterpenes, as the major BVOC group, can contribute to forming secondary organic aerosols and influence cloud properties. In this study, we developed a process‐based monoterpene module in the Vegetation Integrative SImulator for Trace gases (VISIT) model by considering the production, storage, and emission of monoterpene as three main processes. We further evaluated the modeled monoterpene emissions against the ecosystem‐level observation data at a half‐hour scale at a Japanese larch (Larix kaempferi) forest site on Mt. Fuji, Japan. The VISIT model performed with relatively higher accuracy with a Willmott's index of agreement at 0.61, a mean bias error (MBE) at 0.29, and a root mean squared error (RMSE) at 0.43, comparable to that of Model of Emissions of Gases and Aerosols from Nature model with a Willmott's index of agreement at 0.63, a MBE at 0.40, and a RMSE at 0.54. In a long‐term simulation under high CO2 emission scenarios, the ratio between monoterpene emission and gross primary production exhibited a stronger correlation with CO2 concentration than temperature. Our study provides a process‐based modeling approach for more accurately simulating monoterpene emissions from Japanese larch.

The Effect of Laparoscopic Sleeve Gastrectomy on Gastroesophageal Reflux Disease, A Prospective Observational Study

Abstract Background Obesity, defined by the world health organization as a body mass index (BMI) greater than 30, it is an epidemic issue that has major complications on health. It is estimated that more than 200 million men and 500 million women around the world more than 10% of the world’s population are obese. Aim of the Work to examine the effects of LSG on GERD. Depending on gastroesophageal Reflux Disease-Health-Related Quality of Life (GERD-HRQL) questionnaire before and after undergoing elective LSG by 6 months (at least) and result will be confirmed by pH metry for positive cases only (score &amp;gt;35). Patients and Methods This study is a prospective observational study. Conducted patients from outpatient clinic of the general surgery department in Ain Shams university hospitals during the period starting from August 2022, till March 2023. Results Regarding the GERD-HQOL score distribution among study group (n = 100), we found that There were 92 cases (92%) score &amp;lt;35 &amp;8patients (8.0%) score &amp;gt;35; with ranged score 0-38 with mean 14.73±4.27. Regarding the Relation between positive GERD- HQOL score and pH metry (n = 8) among study group, we found that there was a 8 patients were positive GERD-HQOL out of them 7 patients were positive pH metry with agreement 87.5% while there was one cases were negative pH metry, with p-value &amp;lt;0.001 highly significant. Conclusion we found that GERD-HQOL Questionnaire is comparable to pH metry with high index of agreement between questionnaire and pH metry after LSG.

Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

Comparison of Empirical Methods to Estimated Reference Evapotranspiration

Evapotranspiration plays an important role in agricultural water management and crop modelling. Estimating reference Evapotranspiration (ETo) using meteorological variables, both theoretical and empirical methods, is highly recommended considering the availability of weather data in several locations. The estimation method recommended as the standard method is FAO Penman Monteith (FAOPM), but due to the limited meteorological data in a region and the difficulty and complexity of FAOPM, it is recommended to use the empirical method which is easier and only requires a few simple meteorological variables. The aim of this research is to compare and evaluated empirical methods for estimating ETo against the FAOPM. The statistical analysis using in this research are RSME, MAE, coefficient Correlation, NSE, Average bias, index of agreement, and confidence index (c). Evaluation for the best models based on statistic analyzed shows that several empirical methods show terrible performance in estimating the monthly average ETo (mm/day), which are Thornthwaite-Mather, Hargraves-Samani, Makkink, Hamon, Romaneko, and Kharauffa. Modified Blaney-Criddle method showed a good performance method, while PMAWS showed very good performance The Turc and Hansen method showed excellent performance with RMSE, MAE, NSE, and C values ​​for the Turc method, are 0.12, 0.11, 0.78, 0.92 respectively, and for the Hansen method are 0.12, 0.1, 0.8, and 0.89 respectively.

Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery

The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022–23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day−1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day−1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model’s capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples.

In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.