High R2 Research Articles

Valorization of coffee agro-industrial residue for biochar production: Use as adsorbent for methylene blue removal

This research explores using thermally modified residual coffee grounds as a bioadsorbent for removing methylene blue (MB) from aqueous solutions. The study investigates the impact of pH on adsorption capacity, emphasizing its significant influence. Kinetic studies reveal that the pseudo-first-order (PFO) model accurately describes the MB removal process, with correlation coefficients (R²) close to 1 for all samples. The Freundlich model provided the best fit for residual coffee grounds, while the Langmuir model was most suitable for biochar 1. The BiDR model provides the best fit with high R² values, indicating efficient removal for biochar 2 and biochar 3. Notably, the research shows that MB adsorption is effective without requiring pH adjustments, thereby reducing environmental impacts. The maximum adsorption capacities were 40.85, 20.35, 19.52 and 41.98 mg.g−1 using residual coffee grounds, biochar 1, biochar 2 and biochar 3, respectively. Overall, this study demonstrates the practicality of using thermally activated coffee grounds as an efficient adsorbent for removing textile dyes from effluents and highlights the potential of alternative uses for coffee grounds and biochar residues in environmental applications.

Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model

Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R2) value (FCR) of 0.787, and also the highest R2 (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.

Investigating the Potential of Nighttime Light Data to Estimate Travel Demand

ABSTRACTTaking the bike‐sharing travel demand (BSTD) as an example, this study investigates the potential of Nighttime Light (NTL) data to optimize forecasting performance and replace the land use factors. Stepwise regression is trained with the travel demand in each unit as the dependent variable, and land use factors are introduced as the independent variable one by one, which finds the set of independent variables. Five machine learning algorithms driven by ensemble learning and decision trees including the GBDT, Random Forecast, Adaboost, Extratrees, and Catboost, are employed and evaluated to achieve comparative analysis of “before considering‐after considering NTL data”. The methodological verification of Beijing city shows: (1) Adaboost and GBDT are superior to all other algorithms, since they generally have the highest R2, lowest RMSE, and lowest absolute MAPE. (2) All methods by employing NTL data obviously optimize the performance of BSTD forecast with decreased RMSE, decreased MAPE, etc. In particular, GBDT performs the best in reducing MSE, with a percentage of −99.99% in the training set and −86.985% in the test set, which AdaBoost, Extratrees, and Catboost follow. (3) Land use factors no longer make sense in predicting BSTD after employing NTL data, and NTL data has covered the roles of land use factors to ensure accuracy. The conclusions presented here enrich our understanding of the relative roles of land use factors and NTL data in travel demand and boost our optimization in traffic prediction in the future.

ФИЗИКО-ХИМИЧЕСКИЕ СВОЙСТВА МОДИФИЦИРОВАННЫХ БИТУМНЫХ ПРАЙМЕРОВ ДЛЯ ПРОТИВОКОРРОЗИОННОЙ ЗАЩИТЫ СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ И ОБОРУДОВАНИЯ

Compositions based on bitumen primers can be used for anti-corrosion protection of agricultural machinery and equipment. Their physicochemical properties were studied. It has been shown that the thickness of coatings, which is responsible for the effectiveness of barrier protection, increases as expected with an increase in the number of applied coating layers, and decreases even with a slight increase in temperature and with an increase in the amount of solvent in the case of coating by dipping. Increasing the amount of solvent added to the bitumen primer from 40 wt. % up to 67 wt. % leads to a reduction in coating thickness by 55 times. Brush application produces thinner finishes than dip application. Moisture absorption for modified BP with 67 wt. % solvent is 1-2 orders of magnitude higher than that of BP with 40 wt. % white spirit, therefore, a decrease in protective effectiveness can be expected for these materials. According to experimental studies, the contact angles for the original BP exceed 900C, which indicates the presence of hydrophobic properties. The type and concentration of additives affect the contact angles. Introduction 5 wt. % of modifying additives, paraffin P2 and Emulgin, reduces the contact angle of BP, the addition of KO-SZhK, on the contrary, increases it, improving hydrophobic properties. BP with 40 wt. % white spirit, including those with additives, shows good adhesion to carbon steel 0.8 kp. At 67 wt. % solvent in the presence of drying oil and unrefined sunflower oil additives, adhesion worsens. Viscosity-temperature dependences in semi-logarithmic coordinates for modified BP are close to linear in the temperature range 20 -60 0C with a high approximation reliability index R2 = 0.9543 - 0.9833. Based on the physicochemical properties, high anti-corrosion properties can be assumed.

The Effect of Project Management System Implementation, BIM Technology, and Cloud Collaboration on Construction Project Efficiency in Riau

This study examines the impact of Project Management System (PMS) implementation, Building Information Modeling (BIM) technology, and Cloud Collaboration on construction project efficiency in Riau, Indonesia. Utilizing a quantitative research design, data were collected from 190 professionals involved in construction projects and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings reveal that all three factors—PMS implementation, BIM technology, and Cloud Collaboration—positively and significantly influence construction project efficiency. Among these, BIM technology demonstrates the strongest impact, followed by Cloud Collaboration and PMS implementation. The high R² value (0.752) indicates that these factors collectively explain a substantial portion of the variance in project efficiency. This study underscores the importance of integrating advanced technological tools and management practices to enhance project outcomes in the construction industry. The results provide practical insights for construction firms in Riau and similar regions, suggesting that the adoption of BIM, Cloud Collaboration, and PMS can lead to significant improvements in project efficiency.

Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.

Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim to establish a prediction model via a combination of machine learning and population PK (PPK) to support personalized medication decisions for critically ill patients. A retrospective study was performed incorporating 33 variables, including PPK parameters (clearance and volume of distribution). Multiple algorithms and Shapley additive explanations were employed for feature selection of variables to determine the strongest driving factors. The performance of each algorithm with PPK parameters was superior to that without PPK parameters. The composition of support vector regression, categorical boosting and a backpropagation neural network (7:2:1) with the highest R2 (0.809) was determined as the final ensemble model. The model included 15 variables after feature selection, of which the predictive performance was superior to that of models considering all variables or using only PPK. The R2, mean absolute error, mean squared error, absolute accuracy (±5 mg/L) and relative accuracy (±30%) of external validation were 0.649, 3.913, 28.347, 76.12% and 76.12%, respectively. Our study offers a non-invasive, fast and cost-effective prediction model of teicoplanin plasma concentration in critically ill patients. The model serves as a fundamental tool for clinicians to determine the effective plasma concentration range of teicoplanin and formulate individualized dosing regimens accordingly.

Rock dynamic strength prediction in cold regions using optimized hybrid algorithmic models

Predicting the dynamic mechanical characteristics of rocks during freeze–thaw cycles (FTC) is crucial for comprehending the damage process of FTC and averting disasters in rock engineering in cold climates. Nevertheless, the conventional mathematical regression approach has constraints in accurately forecasting the dynamic compressive strength (DCS) of rocks under these circumstances. Hence, this study presents an optimized approach by merging the Coati Optimization Algorithm (COA) with Random Forest (RF) to offer a reliable solution for nondestructive prediction of DCS of rocks in cold locations. Initially, a database of the DCS of rocks after a series of FTC was constructed, and these data were obtained by performing the Split Hopkinson Pressure Bar Test on rocks after FTC. The main influencing factors of the test can be summarized into 10, and PCA was employed to decrease the number of dimensions in the dataset, and the microtests were used to explain the mechanism of the main influencing factors. Additionally, the Backpropagation Neural Network and RF are used to construct the prediction model of DCS of rock, and six optimization techniques were employed for optimizing the hyperparameters of the model. Ultimately, the 12 hybrid prediction models underwent a thorough and unbiased evaluation utilizing a range of evaluation indicators. The outcomes of the research concluded that the COA-RF model is most recommended for application in engineering practice, and it achieved the highest score of 10 in the combined score of the training and testing phases, with the lowest RMSE (4.570,8.769), the lowest MAE (3.155,5.653), the lowest MAPE (0.028,0.050), the highest R2 (0.983,0.94).

Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach

Often, prospective tenants need to know the rental price of an apartment, and homeowners need to know how best to price their apartments. This work aims to predict house rental prices in Lagos, Nigeria, using machine learning by examining the relationship between the rental price and features such as the number of bedrooms, bathrooms, toilets, location and house status(newly built, furnished, and/or serviced). Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. This study also revealed that the number of bedrooms and the apartment's location are the most significant predictors, confirmed using the feature importance analysis. The developed model can be used to estimate the rental price of a property in Lagos, Nigeria.

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate Time Series Forecasting

Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.976, and for Air Quality, it reached an R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} score of 0.94. These results highlight the model’s effectiveness in leveraging the strengths of Temporal Convolutional Networks (TCNs) for capturing multi-scale temporal patterns and Bidirectional Long Short-Term Memory (BiLSTMs) for retaining contextual information, thereby enhancing the accuracy of time series forecasting.

Investigating the impact of blanching and salt treatment on the drying kinetics of oyster mushrooms

This study aims to evaluate the effect of drying temperature and drying kinetics of the blanched and salt-treated oyster mushrooms. The drying temperatures were from 40 to 80 °C. Evaluations were done on the moisture content, rehydration ratio, and physical changes. The CHNS and FTIR analyses were conducted under optimized drying conditions. The drying kinetics were evaluated and the result discovered that the best drying condition with a good rehydration ratio (6.3) and good quality was the salt treatment sample dried at 60 °C in 6 h. The drying kinetic was explored in five different kinetics models, and the parabolic model proved to be the best model with the highest R2 (1.000), lowest RMSE (7.0899E-05), and MBE (5.02666E-09). The effective moisture diffusivities obtained were 0.4–1.8 × 10−9 m2/s and the activations energy was 36–45 kJ/mol. The drying and treatment processes significantly impact the intensity of functional groups, carbon, hydrogen, and nitrogen contents. This study provides useful insights into the drying process of oyster mushrooms using oven drying.

Effect of High Deformation without Preheating on Microstructure and Corrosion of Pure Mg

In this study, the relationship between the extrusion ratio and the corrosion resistance of pure Mg deformed using extrusion with an oscillating die (KoBo) without preheating of the initial billet was investigated. The materials investigated in this study were extruded at high deformation ratios, R1 5:1, R2 7:1, and R3 10:1, resulting in significant grain refinement from the very coarse grains formed in the initial billet to a few µm in the KoBo-extruded samples at room temperature, which is not typical for hexagonal structures. Our research clearly shows that KoBo extrusion improves the corrosion performance of pure Mg, but there is no straightforward dependence between the extrusion ratios and corrosion resistance improvement. Although it was expected that the smallest grain size should provide the highest corrosion resistance, the dislocation density accumulated in the grain interiors during deformation at the highest extrusion ratio, R3 10:1, supports dissolution reactions. This, in turn, provides the answers for the greater grain size observed after deformation at R2 7:1, where dynamic recovery prevailed over dynamic recrystallization. This situation led to the annihilation of dislocation, leading to better corrosion resistance of the respective alloy. Therefore, the alloy with the greatest grain size has the best corrosion resistance.

Parametric modeling of resin-bonded sand mold systems using machine learning-based approaches

This study presents the experimental investigations on modeling of the compression strength and permeability of the resin-bonded sand mold system through Machine Learning approaches. The process of constructing the Fuzzy Logic system was automated by utilizing a data-base and rule-base optimized through genetic algorithms, and the recorded datasets. This research employed three AI models, namely Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forests (RF), using the datasets produced by the GA-tuned Fuzzy model. The objective of this study was to assess and compare the predictive capabilities of three different AI models (Artificial Neural Networks, Decision Tree, and Random Forests) in terms of predicting the values of Compression Strength and Permeability. The complete dataset was divided into two separate subsets, referred to as training data and testing data. Based on the findings, it appears that Random Forest (RF) model exhibits promising potential in accurately predicting the desired mold qualities. The model achieved a high R2 value of 0.9487, indicating a strong correlation with the target values. Additionally, the model demonstrated impressively low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 117 and 17.6 points, respectively. Expanding the dataset size may further enhance the efficacy of the models.

LettuceNet: A Novel Deep Learning Approach for Efficient Lettuce Localization and Counting

Traditional lettuce counting relies heavily on manual labor, which is laborious and time-consuming. In this study, a simple and efficient method for localization and counting lettuce is proposed, based only on lettuce field images acquired by an unmanned aerial vehicle (UAV) equipped with an RGB camera. In this method, a new lettuce counting model based on the weak supervised deep learning (DL) approach is developed, called LettuceNet. The LettuceNet network adopts a more lightweight design that relies only on point-level labeled images to train and accurately predict the number and location information of high-density lettuce (i.e., clusters of lettuce with small planting spacing, high leaf overlap, and unclear boundaries between adjacent plants). The proposed LettuceNet is thoroughly assessed in terms of localization and counting accuracy, model efficiency, and generalizability using the Shanghai Academy of Agricultural Sciences-Lettuce (SAAS-L) and the Global Wheat Head Detection (GWHD) datasets. The results demonstrate that LettuceNet achieves superior counting accuracy, localization, and efficiency when employing the enhanced MobileNetV2 as the backbone network. Specifically, the counting accuracy metrics, including mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (nRMSE), and coefficient of determination (R2), reach 2.4486, 4.0247, 0.0276, and 0.9933, respectively, and the F-Score for localization accuracy is an impressive 0.9791. Moreover, the LettuceNet is compared with other existing widely used plant counting methods including Multi-Column Convolutional Neural Network (MCNN), Dilated Convolutional Neural Networks (CSRNets), Scale Aggregation Network (SANet), TasselNet Version 2 (TasselNetV2), and Focal Inverse Distance Transform Maps (FIDTM). The results indicate that our proposed LettuceNet performs the best among all evaluated merits, with 13.27% higher R2 and 72.83% lower nRMSE compared to the second most accurate SANet in terms of counting accuracy. In summary, the proposed LettuceNet has demonstrated great performance in the tasks of localization and counting of high-density lettuce, showing great potential for field application.

Combining proximal and remote sensing to assess 'Calatina' olive water status.

Developing an efficient and sustainable precision irrigation strategy is crucial in contemporary agriculture. This study aimed to combine proximal and remote sensing techniques to show the benefits of using both monitoring methods, simultaneously assessing the water status and response of 'Calatina' olive under two distinct irrigation levels: full irrigation (FI), and drought stress (DS, -3 to -4 MPa). Stem water potential (Ψstem) and stomatal conductance (gs) were monitored weekly as reference indicators of plant water status. Crop water stress index (CWSI) and stomatal conductance index (Ig) were calculated through ground-based infrared thermography. Fruit gauges were used to monitor continuously fruit growth and data were converted in fruit daily weight fluctuations (ΔW) and relative growth rate (RGR). Normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), green normalized difference vegetation index (GNDVI), chlorophyll vegetation index (CVI), modified soil-adjusted vegetation index (MSAVI), water index (WI), normalized difference greenness index (NDGI) and green index (GI) were calculated from data collected by UAV-mounted multispectral camera. Data obtained from proximal sensing were correlated with both Ψstem and gs, while remote sensing data were correlated only with Ψstem. Regression analysis showed that both CWSI and Ig proved to be reliable indicators of Ψstem and gs. Of the two fruit growth parameters, ΔW exhibited a stronger relationship, primarily with Ψstem. Finally, NDVI, GNDVI, WI and NDRE emerged as the vegetation indices that correlated most strongly with Ψstem, achieving high R2 values. Combining proximal and remote sensing indices suggested two valid approaches: a more simplified one involving the use of CWSI and either NDVI or WI, and a more comprehensive one involving CWSI and ΔW as proximal indices, along with WI as a multispectral index. Further studies on combining proximal and remote sensing data will be necessary in order to find strategic combinations of sensors and establish intervention thresholds.

Explainable machine learning-driven predictive performance and process parameter optimization for caproic acid production

In this study, four machine learning (ML) prediction models were developed to predict and optimize the production performance of caproic acid based on substrates, products, and process parameters. The XGBoost outperformed others, with a high R2 of 0.998 on the training set and 0.885 on the test set. Feature importance analysis revealed hydraulic retention time (HRT) and butyric acid concentration are decisive. The SHAP method offered profound insights into the interplay and cumulative effects of substrate composition, identified the synergistic effects between butyric acid and lactic acid, and emphasized adding glucose can benefit caproic with lactic acid co-fermentation. By integrating the Adaptive Variation Particle Swarm Optimization (AVPSO) algorithm, the optimal process conditions to achieve a maximum caproic acid production of 8.64 g/L was obtained. This study not only advances caproic acid production but contributes a versatile ML-driven strategy applicable to bioprocess optimizations, potentially transformative for sustainable and economically viable bioproduction.

Application of machine learning approaches to predict ammonium nitrogen transport in different soil types and evaluate the contribution of control factors

The loss of nitrogen in soil damages the environment. Clarifying the mechanism of ammonium nitrogen (NH4+-N) transport in soil and increasing the fixation of NH4+-N after N application are effective methods for improving N use efficiency. However, the main factors are not easily identified because of the complicated transport and retardation factors in different soils. This study employed machine learning (ML) to identify the main influencing factors that contribute to the retardation factor (Rf) of NH4+-N in soil. First, NH4+-N transport in the soil was investigated using column experiments and a transport model. The Rf (1.29 – 17.42) was calculated and used as a proxy for the efficacy of NH4+-N transport. Second, the physicochemical parameters of the soil were determined and screened using lasso and ridge regressions as inputs for the ML model. Third, six machine learning models were evaluated: Adaptive Boosting, Extreme Gradient Boosting (XGB), Random Forest, Gradient Boosting Regression, Multilayer Perceptron, and Support Vector Regression. The optimal ML model of the XGB model with a low mean absolute error (0.81), mean squared error (0.50), and high test r2 (0.97) was obtained by random sampling and five-fold cross-validation. Finally, SHapely Additive exPlanations, entropy-based feature importance, and permutation characteristic importance were used for global interpretation. The cation exchange capacity (CEC), total organic carbon (TOC), and Kaolin had the greatest effects on NH4+-N transport in the soil. The accumulated local effect offered a fundamental insight: When CEC > 6 cmol+ kg−1, and TOC > 40 g kg−1, the maximum resistance to NH4+-N transport within the soil was observed. This study provides a novel approach for predicting the impact of the soil environment on NH4+-N transport and guiding the establishment of an early-warning system of nutrient loss.

A preliminary study: Microbial electrolysis cell assisted anaerobic digestion for biogas production from Indonesian tofu-processing wastewater at various Fe additions

Tofu-processing wastewater (TPW) is very abundant in Indonesia. This study aimed to examine the impact of Fe additions (0–600 mg-FeCl3/L) on biogas yield from TPW through anaerobic digestion (AD) without and with microbial electrolysis cells (MEC). The presence of MEC in AD increased biogas yield by 14.4–114.5 %. The optimum Fe addition either in AD or MEC-AD was 400 mg-FeCl3/L. Prediction accuracy was higher for the modified Gompertz model compared to first-order and modified Logistic models because it had a higher R2 (0.9942). The presence of MEC in AD increased ym, μ, and λ values. MEC-AD resulted in pollutant removal efficiencies 1.09 to 1.63 times higher than AD. Based on energy analysis, AD and MEC-AD resulted in surplus energy of 3582.1 and 5325.1 J, respectively. It means that the presence of MC in AD successfully increased the surplus energy by about 1.5 times.

Development of Zeolite Imidazole Framework-Based Adsorbent for Effective Microextraction and Preconcentration of Histamine in Food Samples.

This study is the first to focus on the preconcentration and determination of histamine (HIS) in food samples using zeolite imidazole frameworks (ZIFs) on a solid-phase microextraction (SPME) platform. ZIF was developed on a polypropylene hollow fiber (PPHF) substrate (ZIF@PPHF) and characterized. The extraction performance was optimized by adjusting several parameters, including pH, contact time for adsorption, and desorption conditions. Under the optimized conditions, a wide linear dynamic range (0.05-250 mg/L) with high R2 values (0.9989), low limit of detection (0.019 mg/L), and low limit of quantification (0.050 mg/L) were determined as analytical figures of merit. Additionally, a reusability study confirmed that ZIF@PPHF preconcentrated 83% of the HIS up to the fourth cycle. The developed method was used to preconcentrate HIS in fish and cheese samples. The spiked real samples confirmed the validity and accuracy of this method. The percentage mean recoveries ± relative standard deviation (% RSD, n = 3) at the concentration levels of 5, 10, and 50 mg/L of HIS and the sample amount of 5 g for intra- and inter days ranged from 97 ± 1.10 to 102.80 ± 0.90 and from 96.40 ± 1.82 to 103.40 ± 0.79, respectively. The results suggest that the analytical method validation parameters were acceptable, indicating the repeatability and sensitivity of the method.

Optimal interpolation approach for groundwater depth estimation

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques – inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) – in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R2 (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R2= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R2= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management.The methodological approach comprised:•Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area.•Cross-validation through randomly withholding 20 % of the data for validation purposes.•Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2).

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.