Multi-model Approach Research Articles

Incidence of agroecological-based practices in urban vegetable gardens: effect on crop diversity and production levels

ABSTRACT Agroecological-based practices (ABPs) play a vital role in urban vegetable gardens, enhancing the quality of life. However, factors driving ABP implementation and their impact on crop production and diversity are not fully understood. Our study assessed the influence of socio-economic and environmental characteristics on ABP adoption, crop diversity, and total weight vegetable production in San Carlos de Bariloche, Argentina. Through interviews and a multi-model inference approach, we found that ABP implementation is tied to gardener experience and formative assets (e.g. horticultural experience, specific training). Crop diversity, integral to ABP, is affected by farming practices and farmer characteristics (Farm animals, Pollinator-friendly practices, Experience, Additional training and Environmental knowledge). The Total weight produced was primarily influenced by the type of garden, the gardener’s experience and some ABPs. Long-standing commercial and community objectives and the use of farm animals and commercial seed led to greater production. Strengthening key drivers, such as farmer training, through public policies (e.g. income generation, financing, specific training, land access) can bolster the role of urban gardens in providing a healthy environment and promoting food sovereignty.

A multi-model approach integrating whole-slide imaging and clinicopathologic features to predict breast cancer recurrence risk.

Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor positive/HER2 negative breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole-slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low-risk and high-risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 956 hematoxylin and eosin-stained whole-slide images of 950 ER+/HER2- breast cancer patients with corresponding clinicopathological features that had prior Oncotype DX testing. The model's performance was evaluated using an internal test set of 192 patients from Dartmouth Health and an external test set of 405 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.91 (95% CI: 0.87-0.95) on the internal set and an AUC of 0.84 (95% CI: 0.78-0.89) on the external cohort for predicting low- and high-breast cancer recurrence risk categories based on the Oncotype DX recurrence score. With further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes.

Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI.Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis.Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min.Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available athttps://github.com/JHU-MICA/DCT-UNet.

Structural and Functional Characterization of a Novel Class A Flavin Monooxygenase from Bacillus niacini.

A gene cluster responsible for the degradation of nicotinic acid (NA) in Bacillus niacini has recently been identified, and the structures and functions of the resulting enzymes are currently being evaluated to establish pathway intermediates. One of the genes within this cluster encodes a flavin monooxygenase (BnFMO) that is hypothesized to catalyze a hydroxylation reaction. Kinetic analyses of the recombinantly purified BnFMO suggest that this enzyme catalyzes the hydroxylation of 2,6-dihydroxynicotinic acid (2,6-DHNA) or 2,6-dihydroxypyridine (2,6-DHP), which is formed spontaneously by the decarboxylation of 2,6-DHNA. To understand the details of this hydroxylation reaction, we determined the structure of BnFMO using a multimodel approach combining protein X-ray crystallography and cryo-electron microscopy (cryo-EM). A liganded BnFMO cryo-EM structure was obtained in the presence of 2,6-DHP, allowing us to make predictions about potential catalytic residues. The structural data demonstrate that BnFMO is trimeric, which is unusual for Class A flavin monooxygenases. In both the electron density and coulomb potential maps, a region at the trimeric interface was observed that was consistent with and modeled as lipid molecules. High-resolution mass spectral analysis suggests that there is a mixture of phosphatidylethanolamine and phosphatidylglycerol lipids present. Together, these data provide insights into the molecular details of the central hydroxylation reaction unique to the aerobic degradation of NA in Bacillus niacini.

Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

Abstract Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as postprocessing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and 2-m temperature 2 weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multimodel approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability. Significance Statement Accurately forecasting temperature and precipitation on subseasonal time scales—2 weeks–2 months in advance—is extremely challenging. These forecasts would have immense value in agriculture, insurance, and economics. Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged physics-based predictions and observational data 2 weeks in advance for the entire continental United States. For lagged ensembles, the proposed models outperform standard benchmarks such as historical averages and averages of physics-based predictions. Our findings suggest that utilizing the full set of physics-based predictions instead of the average enhances the accuracy of the final forecast.

Multi-model transfer function approach tuned by PSO for predicting stock market implied volatility explained by uncertainty indexes

This paper studies the forecasting power of uncertainty emanating from the commodities market, energy market, economic policy, and geopolitical threats to the CBOE Volatility Index (VIX). In this study, the relationship between the various uncertainty metrics throughout the period 2012—2022, using a multi-model transfer function technique optimized by particle swarm optimization (PSO) is estimated. Furthermore, we utilize PSO for parameter optimization within the multi-model framework, improving model performance and convergence speed. According to empirical findings, the CBOE Volatility Index reacts nonlinearly to the uncertainty indices. Specifically, the conclusions of the performance metrics show that the OVX index (MAPE: 4.1559%; RMSE: 1.0476% and W: 96.74%) outperforms the geopolitical risk index, the Bloomberg energy index, and the economic policy uncertainty index in predicting the volatility of the US equities market. Although individual models have generated respectful performance, results from the aggregate simulation show that when all predictors are combined, they simultaneously provide better performance indicators (MAVE: 2.7511 %; RMSE: 0.7361%; R2: 98.93%) than when they are estimated separately. In addition, results provide evidence that, when considering non-linear patterns in the data, the multi-model transfer function technique calibrated using PSO demonstrates its outperformance over autoregressive baseline models, traditional econometric models, and deep learning techniques. The effectiveness and accuracy of the multi-model transfer function method tuned by PSO as a forecasting tool are confirmed by the convergence analysis of the cost function. Our methodology innovates by employing a multi-model transfer function technique, which captures the complex and nonlinear relationships between uncertainty indicators and the VIX more comprehensively than traditional single-model approaches. These results are important for traders in terms of hedging as well as portfolio diversification by investing in defensive equities and for policymakers in terms of reliability and preciseness of volatility forecasts.

Groundwater Level Prediction Using Machine Learning and Geostatistical Interpolation Models

Given the vulnerability of surface water to the direct impacts of climate change, the accurate prediction of groundwater levels has become increasingly important, particularly for dry regions, offering significant resource management benefits. This study presents the first statewide groundwater level anomaly (GWLA) prediction for Arizona across its two distinct aquifer types—unconsolidated sand and gravel aquifers and rock aquifers. Machine learning (ML) models were combined with empirical Bayesian kriging (EBK) geostatistical interpolation models to predict monthly GWLAs between January 2010 and December 2019. Model evaluations were based on the Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) metrics. With average NSE/R2 values of 0.62/0.63 and 0.72/0.76 during the validation and test phases, respectively, our multi-model approach demonstrated satisfactory performance, and the predictive accuracy was much higher for the unconsolidated sand and gravel aquifers. By employing a remote sensing-based approach, our proposed model design can be replicated for similar climates globally, and hydrologically data-sparse and remote areas of the world are not left out.

A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency.

Enhancing real-time intrusion detection and secure key distribution using multi-model machine learning approach for mitigating confidentiality threats

Ensuring strong security measures against intrusions is of utmost importance in the ever-changing field of information management systems. Conventional Intrusion Detection Systems (IDS) frequently have difficulties in dealing with the ever-changing and intricate characteristics of contemporary cyber threats, particularly in the realm of the Internet of Things (IoT). The current body of research emphasizes the difficulties in attaining both high precision and real-time speed while still preserving the anonymity of data. This work tackles these concerns by presenting a scalable multi-model Machine Learning (ML) technique developed to improve real-time intrusion detection and ensure safe cryptographic key distribution. The suggested solution takes use of the widespread use of IoT devices, which increases the likelihood of advanced cyberattacks. Our approach involves implementing a ML-based automated IDS specifically designed for various IoT environments. These IDS enhance adaptability and accuracy. We also utilize Maximum–Minimum (Max–Min) normalization on the UNSW-NB15 and CICIoT2023 datasets to improve the accuracy of detecting intrusions. Furthermore, we classify a wide range of contemporary threats and typical internet traffic into nine distinct attack categories. To streamline data processing and improve system efficiency, we employ Principal Component Analysis (PCA) for dimensionality reduction. Additionally, we deploy six advanced ML models to optimize detection capabilities and accurately identify threats. Finally, we develop a secure key distribution mechanism using synchronized Artificial Neural Networks (ANNs). The process of mutual learning guarantees the secure distribution of keys among IoT networks, thus reducing the risks to secrecy. This novel methodology not only reinforces the ability to identify intrusions in real-time, but also improves the overall security stance of information management systems. This work significantly contributes to the field of digital security in information management by addressing the limits of current IDS solutions and presenting a complete, multi-faceted security strategy.

Assessment of landslide susceptibility in the Himalayan state of Tripura, India, using a Multi-Model Approach

Landslides are the down slope mass movement of soil, rocks, and debris due to a natural or human activities resulting in widespread hazard events in India. The most affected areas comprise 15 percent of its landmass which includes Tripura and eleven Himalayan states and parts of the Western and Eastern Ghats in India. In Tripura, landslides cause road blockage and destruction of settlements, bringing economic and life losses in every year. Thus, this research is focused on identifying landslide susceptible zones and the significant causative factors behind landslides. Assessment of Landslide Susceptibility (LS) identifies fifteen major causative factors under five broad groups; topographic, geotechnical, hydrological, environmental, and anthropogenic. With application of Analytical Hierarchical Process (AHP), Frequency Ratio (FR), and Random Forest (RF)-based models were performed to extract landslide susceptible zonation map for Tripura. This study reveals that the successive hill ranges formed by young sedimentary lithologic formations associated with deforestation, heavy rainfall during monsoon, and anthropogenic activities (road constructions and jhumming) are the responsible geo-conditions for triggering landslides. In this study, while the AHP and FR model show only 1.95% and 11.46% confined along the hilltop of Jampui, Sakhan, and Longtarai, the RF model designated Tripura’s 30% land area as high and very high landslide susceptible zones (LSZ), predominantly over hills, foothills, and low laying undulating land (tillas). For the accuracy assessment, the ROC curve is used, which shows that RF model appears to be the maximum accurate (0.810) one, followed by FR (0.806) and AHP (0.744).

Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging

Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon's Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with potential for application in other regions worldwide.

Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan

While socioeconomic gradients in regional health inequalities are firmly established, the synergistic interactions between socioeconomic deprivation and climate vulnerability within convenient proximity and neighbourhood locations with health disparities remain poorly explored and thus require deep understanding within a regional context. Furthermore, disregarding the importance of spatial spillover effects and nonlinear effects of covariates on childhood stunting are inevitable in dealing with an enduring issue of regional health inequalities. The present study aims to investigate the spatial inequalities in childhood stunting at the district level in Pakistan and validate the importance of spatial lag in predicting childhood stunting. Furthermore, it examines the presence of any nonlinear relationships among the selected independent features with childhood stunting. The study utilized data related to socioeconomic features from MICS 2017–2018 and climatic data from Integrated Contextual Analysis. A multi-model approach was employed to address the research questions, which included Ordinary Least Squares Regression (OLS), various Spatial Models, Machine Learning Algorithms and Explainable Artificial Intelligence methods. Firstly, OLS was used to analyse and test the linear relationships among selected variables. Secondly, Spatial Durbin Error Model (SDEM) was used to detect and capture the impact of spatial spillover on childhood stunting. Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. Finally, EXAI methods such as SHapley were utilized to identify potential nonlinear relationships. The study found a clear pattern of spatial clustering and geographical disparities in childhood stunting, with multidimensional poverty, high climate vulnerability and early marriage worsening childhood stunting. In contrast, low climate vulnerability, high exposure to mass media and high women’s literacy were found to reduce childhood stunting. The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. Furthermore, EXAI methods such as partial dependency plot reveal the existence of a nonlinear relationship between multidimensional poverty and childhood stunting. The study’s findings provide valuable insights into the spatial distribution of childhood stunting in Pakistan, emphasizing the importance of considering spatial effects in predicting childhood stunting. Individual and household-level factors such as exposure to mass media and women’s literacy have shown positive implications for childhood stunting. It further provides a justification for the usage of EXAI methods to draw better insights and propose customised intervention policies accordingly.

Integrating a multi-variable scenario with Attention-LSTM model to forecast long-term coastal beach erosion

Beach erosion is an adverse impact of climate change and human development activities. Effective beach management necessitates integrating natural and anthropogenic factors to address future erosion trends, while most current prediction models focus only on natural factors, which may provide an incomplete and potentially inaccurate representation of erosion dynamics. This study enhances prediction methods by integrating both natural and anthropogenic factors, thereby enhancing the accuracy and reliability of erosion projections. By extracting historical shorelines through CoastSat model from 1986 to 2020, we develop multivariable scenarios with Attention-LSTM model to predict the regional impacts of natural and anthropogenic factors on erosion to sandy beaches along the typical shoreline of Shenzhen in China. Results reveal that Shenzhen's beaches experienced erosion up to 12 m over the past 35 years. Here we project a decrease in the mean erosion rate of the beaches, identifying population growth (21.0 %) as the main controlling factor before the mid-century in a range of scenarios. We find that Attention-LSTM multi-model ensemble approach can provide overall improved accuracy and reliability over a wide range of beach erosion compared to scenario prediction model of Attention-LSTM and statistical model of Digital Shoreline Analysis System (DSAS), yielding an average uncertainty of 10.99 compared to 13.29. These insights reveal policies to safeguard beaches because of the rising demand for beaches due to human factors, coupled with decreased impervious surfaces through ecological conservation, lead to mitigation for beach erosion. Accurate forecasts empower policymakers to implement effective coastal management strategies, safeguard resources, and mitigate erosion's adverse effects. Our study offers finely-tuned predictions of coastal erosion, providing crucial insights for future coastal conservation efforts and climate change adaptation along the shoreline, and serving as a foundation for further research aimed at understanding the evolving environmental impacts of beach erosion in Shenzhen.

Analysis of climate change and climate variability impacts on coastal storms induced by extratropical cyclones: a case study of the August 2015 storm in central Chile

ABSTRACT The projected increase in coastal risk requires a reevaluation of coastal risk reduction strategies. A multi-model approach is proposed to examine the variability of coastal storms influenced by climate change and El Niño Southern Oscillation (ENSO). To this end, the historic coastal storm of August 8 2015, resulting from a local extratropical cyclone (ETC) off the central Chilean coast, was analyzed through the coupling of the WRF atmospheric model, Delft3D FM (D-FLOW and D-WAVE modules), and EOT20 astronomical tide model. The results show that the characteristics of local ETCs are susceptible to regional temperature gradients associated with climate change and ENSO. The coastal storm of August 8 2015, presented a decrease in wave height and counterclockwise rotation of wave direction along the Chilean coast under the climate change scenario. Meanwhile, the ENSO scenarios under cold conditions generated a ETC track’s displacement toward the north, causing both an increase in wave height along the coast of the Antofagasta and Atacama regions and a decrease in wave height in the Valparaíso, O’Higgins, and Maule regions. Findings from this study emphasize the importance of considering dynamic design for coastal structures rather than traditional methods to adapt to changing storm patterns.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.

Sensitivity of montane grassland water fluxes to warming and elevated CO2 from local to catchment scale: A case study from the Austrian Alps

Study region: Montane grassland within the Gulling catchment, Austrian Alps. Study focus: A climate-change experiment in a grassland ecosystem used lysimeters and HYDRUS-1D models to quantify changes in evapotranspiration (ET) and groundwater recharge (GWR) due to warming (＋3 °C) and elevated CO2 concentrations (ΔCO2; ＋300 ppm). Findings at the plot-scale were generalized and transferred to the surrounding catchment, half comprised of grassland, using three lumped rainfall–runoff models and two spatially-distributed Community Water Models, differing in soil hydraulic properties.New hydrological insights for the region: Warming increased ET and decreased GWR and river discharge compared to ambient conditions. ΔCO2 increased stomatal resistance, which partially offset warming effects. In scenarios combining warming and ΔCO2, the impact of warming was higher than ΔCO2 effect. Elevation influenced the sensitivity of ET to warming, which was greater at the catchment scale than at the plot scale, while GWR was more sensitive to warming at the plot scale. Under dry conditions, GWR and discharge exhibited increased sensitivity to warming at both scales. HYDRUS-1D successfully reproduced lysimeter experiment results and their sensitivity to warming and ΔCO2. Despite model agreement on water flux sensitivity to climate changes, the varying response magnitudes highlight the need for a multi-model approach in climate impact assessments. This study provides insights into how climate change might impact hydrological dynamics of montane grassland systems across the Central European Alps.

Machine learning to guide the use of plasma technology for antibiotic degradation

Antibiotics are misused and discharged into environmental water, posing a constant potential threat to the ecosystem. Utilising plasma’s physical and chemical effects to remove antibiotics has emerged as a promising wastewater treatment technology. However, the complexity and high cost of reactor configurations represent significant limitations to the practical application of this technology. Furthermore, evaluating the degradation efficiency of antibiotics necessitates using costly and sophisticated testing instruments, coupled with time-consuming and labour-intensive experiments. The present study developed a generalised model using machine learning algorithms to predict the removal efficiency of antibiotics by a plasma system. Of the eight machine learning algorithms constructed, the ensemble model XGBoost exhibited the highest prediction accuracy, as indicated by a Pearson correlation coefficient of 0.943. This correlation indicates a strong relationship between the predicted removal rates and the experimental values. Moreover, the accuracy of the prediction was enhanced through the utilisation of a multi-model stacking approach. A further quantitative assessment of the key factors affecting the efficiency of the plasma process, and their synergistic effects, is provided by the interpretable analysis of the model’s behaviour. It is anticipated that the results will facilitate the design of efficient plasma systems, reduce the need for extensive experimental screening, and improve practical applications in the removal of antibiotic contamination. This provides an informative view of the applications of plasma technology, opening the way for new environmental research questions.

Impact of multi-model machine learning approach prediction of Covid-19

A present study was to analyzed the “Impact of multi-model machine learning approach predication of Covid-19.” Materials and Methods has been used to a cluster sample from secondary source. A data was taken 250 Covid- 19 simple from cross-sectional online survey was conducted for A.N.C.H. Gaya, in the month of August 2020. Both of the simple size the age range of 25 to 65 years and including interns were enrolled in the study. The study was approved by the ministry of India and Arogya Setu application and medical institutional ethics committee. Online informed consent was obtained from each participant and the information was gathered using a google form administered open ended questionnaire (5 items) to the participants. Out of 500 participants from A.N.C.H. Gaya, while 250 participated and filled the questionnaire completely. All the data were collected digitally and analyzed for the results.

Scenario projections of South Asian migration patterns amidst environmental and socioeconomic change

Projecting migration is challenging, due to the context-specific and discontinuous relations between migration and the socioeconomic and environmental conditions that drive this process. Here, we investigate the usefulness of Machine Learning (ML) Random Forest (RF) models to develop three net migration scenarios in South Asia by 2050 based on historical patterns (2001–2019). The model for the direction of net migration reaches an accuracy of 75%, while the model for the magnitude of migration in percentage reaches an R2 value of 0.44. The variable importance is similar for both models: temperature and built-up land are of primary importance for explaining net migration, aligning with previous research. In all scenarios we find hotspots of in-migration North-western India and hotspots of out-migration in eastern and northern India, parts of Nepal and Sri Lanka, but with disparities across scenarios in other areas. These disparities underscore the challenge of obtaining consistent results from different approaches, which complicates drawing firm conclusions about future migration trajectories. We argue that the application of multi-model approaches is a useful avenue to project future migration dynamics, and to gain insights into the uncertainty and range of plausible outcomes of these processes.

Probabilistic Cascade Modeling for Enhanced Flood and Landslide Hazard Assessment: Integrating Multi-Model Approaches in the La Liboriana River Basin

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential.