Statistical Performance Indices Research Articles

Comparative analysis of ensemble learning algorithms in water quality prediction

ABSTRACT Water is an essential resource necessary for the survival of all life forms, yet it is continually at risk of contamination. Accurate water quality prediction is essential for protecting ecosystem health. This study aims to assess the effectiveness of ensemble learning techniques, namely AdaBoost, gradient boosting, XGBoost, CatBoost, and LightGBM, in predicting water quality parameters in the Bara River Basin, Pakistan. Initially, a random forest model was used to determine the input water quality parameters combination for the selected target water quality variable. Then, the ML models were developed for each combination of input parameters and target water quality variables. The ML model's performance was assessed via statistical performance indicators, namely R2, mean squared error, and mean absolute error. The most suitable model was highlighted using compromise programming. The results reveal that the XGBoost and gradient boosting models outperform other algorithms based on statistical indicators, displaying remarkable predictive ability with near-perfect R2 values for HCO3, CO3, and Mg on the XGBoost model and electrical conductivity, SO4, Temp, and Ca on the gradient boosting model. Whereas CatBoost and LightGBM have a more robust performance on some parameters, such as pH and dissolved solids while its performance in other water quality parameters was weak.

The Impact of Socio-Economic and Climate Change on Poverty in Indonesia

Climate change can impact farmers’ incomes as agricultural production still depends on the weather. Currently, the majority of the impoverished rely primarily on agriculture for their income. The connection between poverty and climate change has been extensively studied, but further research is needed in this area. This research was conducted to provide empirical evidence regarding the impact of climate change on poverty using time series data, which has never been done. This research wants to examine the impact of socio-economics (economic growth, agricultural sector growth, inequality, inflation) and climate change on poverty. This research uses time series data from 2007 to 2022. The Central Bureau of Statistics and Climate Change Performance Index (CCPI) reports are the sources of research data. The study results suggest that the government’s performance index in combating inflation, agricultural sector growth, and climate change has a positive impact on poverty. Poverty is negatively affected by the Gini index and economic growth. Government efforts to adaptively address climate change are necessary to prevent worsening impacts on poverty rates. To reduce the risk of crop failure, farmers must also practice practical agricultural management.

Advanced controller design based on interval type-2 fuzzy neural network for elementary super-lift Luo converter

The elementary super-lift Luo converter is a third-order DC/DC step-up converter developed using the super-lift method that increases input voltage with low current and voltage ripples and a high voltage gain. On account of the nonlinearity and uncertainty incorporated with voltage, load changes, and switching operation of elementary super-lift Luo (ESLL) converter, it is a challenging task to design an efficient controller. To overcome these problems, this study proposes a new control approach based on interval type-2 fuzzy neural network (IT2FNN) to regulate a DC output current and voltage of ESLL converter. The parameters of IT2FNN were trained offline using a gradient descent algorithm. Mean square error (MSE), one of the most used statistical performance indicators, was used to evaluate the convergence accuracy performance of gradient descent algorithm. The DC output voltage regulation performance of IT2FNN was evaluated via a comparative simulation with interval type-2 fuzzy controller (IT2FC) and proportional + integral (PI) controller in MATLAB/Simulink environment in terms of settling time, average computational time, and recovery time under three different operational conditions: various reference voltages, input voltage, and load. Moreover, the DC output current regulation performance of IT2FNN was evaluated via a comparative simulation with IT2FC and PI controller in MATLAB/Simulink environment in terms of settling time and steady-state error under various reference current changes. The detailed simulation results obtained from comparative simulation validated the effectiveness of IT2FNN.

Impact of Financial Inclusion and ESG on the Sustainable Growth of the Nation: Moderating Role of Poverty

The literature has a strong foundation for the contributions that financial inclusion (FI) and environment social governance (ESG) provide to the sustainable growth (SG) of the nation. This study looks at the relationships between FI and ESG with SG through moderating role of poverty in the nation. The study performs a quantitative analysis by utilizing the panel data regression model on 140 countries with a time period of nine years, that is 2011–2019. In this study, models have been tested for consistency with FE (fixed effect) or RE (random effect). In it, ATMs are used as a proxy of FI, DW_EG is a proxy of statistical performance indicators (SPI), Agr_Land are used as a proxy of Environment Social and Governance (ESG) and Pov_surv are used as a proxy of poverty. GDP_grw are used as a proxy of GDP growth (GDP per capita growth) and RP are used as a proxy of rural population. This study establishes three models, that is, Models 1, 2 and 3. The empirical findings demonstrate a significant linkage between ESG, FI and SG, whereas poverty is found insignificant. Further research confirms that the development of careful co-curation of ICT infrastructure, FI programmes and economic growth strategies is necessary to promote nation’s SG.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Label-free assessment of complement-dependent cytotoxicity of therapeutic antibodies via a whole-cell MALDI mass spectrometry bioassay

Potency assessment of monoclonal antibodies or corresponding biosimilars in cell-based assays is an essential prerequisite in biopharmaceutical research and development. However, cellular bioassays are still subject to limitations in sample throughput, speed, and often need costly reagents or labels as they are based on an indirect readout by luminescence or fluorescence. In contrast, whole-cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) Mass Spectrometry (MS) has emerged as a direct, fast and label-free technology for functional drug screening being able to unravel the molecular complexity of cellular response to pharmaceutical reagents. However, this approach has not yet been used for cellular testing of biologicals. In this study, we have conceived, developed and benchmarked a label-free MALDI-MS based cell bioassay workflow for the functional assessment of complement-dependent cytotoxicity (CDC) of Rituximab antibody. By computational evaluation of response profiles followed by subsequent m/z feature annotation via fragmentation analysis and trapped ion mobility MS, we identified adenosine triphosphate and glutathione as readily MS-assessable metabolite markers for CDC and demonstrate that robust concentration–response characteristics can be obtained by MALDI-TOF MS. Statistical assay performance indicators suggest that whole-cell MALDI-TOF MS could complement the toolbox for functional cellular testing of biopharmaceuticals.

Artificial intelligence-driven enhanced CBR modeling of sandy soils considering broad grain size variability

The soil packing, influenced by variations in grain size and the gradation pattern within the soil matrix, plays a crucial role in constituting the mechanical properties of sandy soils. However, previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio (CBRs) of sandy soils by precisely articulating soil packing in the modeling framework. This study presents an innovative artificial intelligence (AI)-based approach for modeling the CBRs of sandy soils, considering grain size variability meticulously. By synthesizing extensive data from multiple sources, i.e. extensive tailored testing program undertaking multiple tests and extant literature, various modeling techniques including genetic expression programming (GEP), multi-expression programming (MEP), support vector machine (SVM), and multi-linear regression (MLR) are utilized to develop models. The research explores two modeling strategies, namely simplified and composite, with the former incorporating only sieve analysis test parameters, while the latter includes compaction test parameters alongside sieve analysis data. The models' performance is assessed using statistical key performance indicators (KPIs). Results indicate that genetic AI-based algorithms, particularly GEP, outperform SVM and conventional regression techniques, effectively capturing complex relationships between input parameters and CBRs. Additionally, the study reveals insights into model performance concerning the number of input parameters, with GEP consistently outperforming other models. External validation and Taylor diagram analysis demonstrate the GEP models' superiority over existing literature models on an independent dataset from the literature. Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBRs, further emphasizing GEP's efficacy in modeling such complexities. This study contributes to enhancing CBRs modeling accuracy for sandy soils, crucial for pertinent infrastructure design and construction rapidly and cost-effectively.

Predictive modeling of fatigue and rutting parameters for asphalt cement modified with pretreated oil palm clinker using artificial neural network algorithms to enhance pavement performance

Currently, the viscoelastic properties of standard asphalt cement cannot sustain the increasing demands resulting from heavier traffic loads, greater stress levels, and changing environmental conditions. Thus, the usage of modifiers is encouraged. Also, the Sustainable Development Goal (SDG) encourages the use of waste resources and emerging technologies in asphalt pavement technology. This study intends to harness this gap by examining the use of oil palm clinker (OPC) as an asphalt-cement modification to improve its viscoelastic properties using an innovative prediction approach. The modified asphalt-cement was produced by varying the acid-treated OPC powder (OPCP) content at 2%, 4%, 6%, and 8% and the rutting and fatigue performance was evaluated. This paper also presents an optimization approach and prediction comparison based on statistical modeling and artificial intelligence (AI) algorithms for the fatigue and rutting parameters of the modified asphalt cement. Model variables for the predictive models include OPCP content and test temperatures. The AI algorithms use 70% of the data for training, 15% for testing, and 15% for validation. The results showed that the incorporation of OPCP improves the properties of pure asphalt-cement by increasing stiffness and temperature susceptibility and that the crystalline phase of Si–O formed a novel structural group Si–OH. The RSM R2 for rutting for unaged and RTFO aged responses was (99.743 and 99.893), the RMSE was (436.210 and 954.945), and the MRE was 3.269 and 2.315) for the model statistical performance index, respectively. The ANN R2 for rutting for unaged and RTFO aged responses were (99.903 and 99.970) the RMSE (106.283 and 528.500) and MRE (1.759 and 1.039). PAV fatigue RSM R2 values were (99.984), RMSE (77979.750), and MRE (12.089), while ANN R2 values were (99.997), RMSE (53933.500), and MRE (5.262). The findings demonstrated that the generated model and algorithm could predict the fatigue and rutting performance of the OPCP-modified asphalt cement accurately with the AI algorithms model outperforming the statistical model. Also, the study aligns with SDG 9 by developing advanced modeling techniques and enhancing infrastructure durability through innovative use of modified materials as well as SDG 12 by incorporating recycled materials into sustainable production practices.

Is there a quantitative relationship between democracy and official statistics?

The main purpose of this article is to show the quantitative relationship between political regimes and the quality of the national statistical systems. The data exploratory analysis, usually treated from a qualitative point of view, shows a strong correlation between democracy and official statistics, a thesis confirmed in all continents. The most democratic countries are the ones with the best statistical performance. In fact, among the top 10 democracies, five are also in the top 10 for statistical performance. The correlation between democracy and statistical performance is about 70 percent, although over the years 2016–2022, it has been slightly decreasing. The country’s statistical performance is affected by political regime. The main indicators employed for this analysis are the Democracy Index by The Economist, and the Statistical Performance Indicators by the World Bank. The use of these global indicators, encompassing an entire range of years and countries, is unusual in statistical analysis.

Intercomparison of deep learning models in predicting streamflow patterns: insight from CMIP6

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (Tmax), Minimum Temperature (Tmin), and precipitation for the aforementioned three techniques. The best MME for Tmax, Tmin, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R2 during training (65.25 m3/s, 4256.97 m3/s, 46.793 m3/s and 0.7978) and testing (72.06 m3/s, 5192.95 m3/s, 51.363 m3/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

Exploring the impact of thiol collectors system on copper sulfide flotation through machine learning-driven modeling

Collector selection is a critical step in flotation, as it has a direct impact on product quality, flotation recovery, and selectivity. Collectors can consist of different components, and their effectiveness can vary depending on the type of ore being processed. The general practice in both literature and in industry is to use a mixture of collectors rather than a single collector. However, the use of a collector mixture introduces several complex issues. It is challenging to determine the specific effects of each collector on different minerals, as well as to understand the synergistic effects of mixed collectors in flotation. This study presents a novel investigation focusing on the impact of blends of NAX, AEROPHINE® 3422, and AERO® MX 5149, in varying dosages and combinations, on the flotation performance of Kupferschiefer copper ore. Kinetics flotation tests were conducted using a mechanical flotation cell with various combinations and dosages of listed collectors. For this investigation, different predictive models such as machine-learning (ML) and conventional regression analyses were developed. For model construction, a database including the results of comprehensive experimental results was constructed. The best performing model was selected considering statistical performance indicators and their performance on unseen data. A sensitivity analysis was conducted on the model to justify contributions of collectors on the copper recovery and grade. The results showed that the ML-based models provide compatible results with the expert opinions and have higher statistical performance than conventional modelling tools. According to the experimental results and models’ findings, it has shown that AEROPHINE® 3422 (a blend of isopropyl ethyl thionocarbamate and dithiophosphinate) was the most influential collector for the copper recovery. In addition, two ternary graphs were generated from the modeled data to formulate mixtures for different grades and recovery priorities.

Assessment of the Quality of Cause-of-Death Data in Serbia for 2005-2019 Vital Statistics Performance Index Estimation.

We aimed to evaluate the quality of the cause of death (COD) concerning mortality patterns and completeness of death registration to identify areas for improvement in Serbia. COD data collected from the mortality register in Serbia from 2005 to 2019 (1540615 deaths) were analyzed with the software Analysis of National Causes of Death for Action. The Vital Statistics Performance Index for Quality (VSPI(Q)) is estimated for the overall COD data quality. The completeness of death certification was higher than 98%. Usable underlying COD was registered in 57%, 24.1% with an unusable and 18.6% with insufficiently specified COD. The VSPI(Q) was 67.2%, denoting medium quality. The typical error was using intermediate COD (24.7% of all deaths), while 13.2% and 8.5% of all garbage codes (GC) belonged to the Very High and High Severity classes. The leading underlying COD is unspecified cardiomyopathy. The analysis revealed that 39.1% of GC has been redistributed to non-communicable diseases, 2.5% to external causes and 1.1% to communicable diseases. In the 15 years' worth of data analyzed, the true underlying COD, in many cases, was ill-defined, indicating that COD data at the national level could be distorted. The additional and continuous professional education of medical students as well as physicians is needed. It should focus on the most common GC among the leading COD and acquiring skills in certifying external causes of death.

The trend of the quality of cause-of-death data and its association with socio-economic indicators in Serbia in the period 2005–19

This study aims to evaluate the temporal trend in the quality of cause-of-death data and garbage code profiles and to determine its association with socio-economic status in Serbia. A longitudinal study was assessed using data from mortality registers from 2005 to 2019. Computer application Analysis of Causes of National Deaths for Action (ANACONDA) calculates the distribution of garbage codes by severity and composite quality indicator: Vital Statistics Performance Index for Quality (VSPI(Q)). A relationship between VSPI(Q) and country development was estimated by analysing two socio-economic indicators: the Socio-demographic Index and the Human Development Index (HDI). Serbia indicates progress in strengthening cause-of-death statistics. The steady upward trend of the VSPI(Q) index has risen from 55.6 (medium quality) to 70.2 (high quality) over the examined years. Significant reduction of ‘Insufficiently specified causes with limited impact’ (Level 4) and an increase in the trend of ‘High-impact garbage codes’ (Levels 1 to 3) were evident. Decreased deaths of no policy value (annual percentage change of −1.41%) have manifested since 2014. A strong positive association between VSPI(Q) and socio-economic indicators was assessed, where the HDI has shown a stronger association with VSPI(Q). Improved socio-economic conditions on the national level are followed by enhanced cause-of-death data quality. Upcoming actions to improve quality should be directed at high-impact garbage codes. The study underlines the need to prioritise the education and training of physicians with a crucial role in death certification to overcome many cause-of-death quality issues identified in this assessment.

Country statistical capacity: A recent assessment tool and further reflections on the way forward1

A country’s statistical capacity takes an indispensable part in its development. We offer a comprehensive comparison between the World Bank’s Statistical Performance Indicators and Index (SPI) and its predecessor, the Statistical Capacity Index (SCI) regarding different conceptual and empirical aspects. We further examine the relationships of the two indexes with some agriculture development indicators such as food security, food sustainability and productivity as well as other key indicators including headcount poverty, GDP per capita, and an SDG progress index. Our analysis employs the latest SPI data update in 2022, which were not available in previous studies. We also propose clear guidelines on how the SPI can be maintained and updated in the future to ensure that this process is transparent, replicable, safeguarded with high quality, and provides comparable data over time.

Flood inundation mapping under climate change scenarios: insights from CMIP6

ABSTRACT The present research endeavors to simulate daily stream flow by employing hydrologic and hydraulic modeling techniques to comprehensively assess the impact of climate change on flood risk. This investigation was conducted within the Shekhan basin, situated in the eighth zone of Jalalabad City, Afghanistan. The efficacy of the HEC-HMS model was meticulously evaluated for each individual flood event during both calibration (Jan/2015-October/2019) and validation (November/2019-July/2022) phases using various statistical performance indicators, notably the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). During calibration, the HEC-HMS model yielded R2, NSE, and PBIAS values of 0.8795, 0.86, and 12%, respectively, while during validation, these metrics stood at 0.85, 0.8, and 9%, respectively. Among the five GCM models (INM-CM4-8, INM-CM5-0, MIROC6, MPI-ESM1-2-LR, MRI-ESM-2-0) examined, the MPI-ESM1-2-LR demonstrated superior performance based on Taylor Skill Score and Rating Metric analysis. Additionally, the HEC-SSP was employed to scrutinize precipitation frequency and to fit ranking distributions for GCM SSP245 and SSP585 scenarios. Subsequently, the aforementioned GCM data were utilized in hydrologic modeling to generate hydrographs for various return periods, while hydraulic modeling via the HEC-RAS 2D model facilitated the creation of flood inundation maps for different return periods.

Validation of the MGB-IPH hydrological model for flows simulation in paired watershed in minas gerais, Brazil.

Simulating hydrological behavior makes it possible to analyze and predict water availability in river basins, anticipate potential events and carry out quantitative water balances using various scenarios. These scenarios arise from changes in land use and landscape over the years, the occurrence of extreme events and the effects of climate change. In this context, the aim of this study was to evaluate the effectiveness of the Large Basin Hydrological Model (MGB-IPH) for representing rainfall-runoff in the paired watershed of the Piracicaba River and Piranga River in the state of Minas Gerais, Brazil, using the usual calibration and validation procedures. The results of the average statistical performance indices NASH, NASH-Log, BIAS, R², D and Dr showed that the model was able to simulate the seasonality of the watersheds and presented good adjustments, with a tendency to overerestimate the flow, but within acceptable limits. It is concluded that the model performed satisfactorily in its ability to simulate the hydrological behaviour of the Piracicaba River and Piranga River watersheds, despite its limitations and the lack of input data, making it an applicable tool in local contexts.

Hydrological dynamics and water resource availability in the Rift Valley Lakes Basin (Ethiopia), using a WEAP model

Abstract This study aims to assess hydrologic dynamics and the water resource potential of the Rift Valley Lake Basin (RVLB) in Ethiopia using the Water Evaluation and Planning System (WEAP) model. The surface water of the RVLB comprises streamflow and an open water system. The model was configured with 13 catchments and a monthly time series of approximately 43 years (1981–2022) to address the spatial variability of rainfall-runoff interaction. Statistical performance indicators were used to evaluate the accuracy of the model in simulating streamflows, and the results showed that the coefficient of determination (R2) ranges from 0.82–0.93, Nash-Sutcliffe coefficient of efficiency (NSE) ranges from 0.68–0.86, percentage of bias (PBIAS) ranges from –9.45 to –1.85, standard deviation ratio (RSR) ranges from 0.35 to 0.59 and index of agreement (IA) ranges from 0.62 to 0.84. The available surface water for abstraction is estimated to be 358 million cubic meters (MCM) available as lake water abstraction, and 6,534 MCM as streamflow water, making it a total surface water flow of 6,892 MCM. Considering the temporal distribution of the surface water sources, 67.5% is available in the rainy season, June–October, and 32.5% during the dry period, November–May, in the basin.

Proficiency of probability distributions in unit hydrograph derivation

Abstract The probability distribution function (PDF)-based unit hydrographs (UHs) are gaining momentum in an application for more accurate rainfall-runoff transformation. Employing seven statistical performance indices, R2, NSE, MSE, RMSE, MAE, MAPE, and SE in generalized reduced gradient nonlinear programming (GRG-NLP) optimization, 18 known and 12 adaptable PDF-based UHs were assessed against UHs derived from 18 storms in 7 basins across the United States, Turkey, and India. To this end, 27 Maple codes were proposed for UH-application requiring only peak discharge (qp), time to peak (tp), and time base (tb) for derivation. The introduced PDFs, such as Dagum, Generalized Gamma, Log-Logistic, Gumbel Type-I, and Shifted Gompertz, replicated the observed data-derived UHs more closely than did the known PDFs, like Inverse Gaussian, two-parameter gamma distribution (2-PGD), Log-Normal, Inverse-Gamma, and Nagakami. Among the three-parameter (6 nos.), two-parameter (21 nos.), and single-parameter (3 nos.) PDFs, the Dagum, Log-Logistic, and Poisson consistently outperformed their respective counterparts in replication.

Improving precipitation estimates for Turkey with multimodel ensemble: a comparison of nonlinear artificial neural network method with linear methods

AbstractEnsemble analysis is proven to provide advantages in climate change impact assessment based on outputs from climate models. Ensembled series are shown to outperform single-model assessments through increased consistency and stability. This study aims to test the improvement of precipitation estimates through the use of ensemble analysis for south and southwestern Turkey which is known to have complex climatic features due to varying topography and interacting climate forcings. The analysis covers an evaluation of the performance of eight regional climate models (RCMs) from the EUR-11 domain available from the CORDEX database. The historical outputs are evaluated for their representativeness of the current climate of the Mediterranean region and its surroundings in Turkey through a comparison with long-term monthly precipitation time series obtained from ground-based precipitation observations by the use of statistical performance indicators and Taylor diagrams. This is followed by a comparative evaluation of three ensemble methodologies, simple average of the models, multiple linear regression for superensemble, and artificial neural networks (ANN). The analysis results show that the overall performance of ensembled time series is better compared to individual RCMs. ANN generally provided the best performance when all RCMs are used as inputs. Improvement in the performance of ensembling due to the use of nonlinear models is further confirmed by fuzzy inference systems (FIS). Both ANN and FIS generated monthly precipitation time series with higher correlations with those of observations. However, extreme events are poorly represented in the ensembled time series, and this may result in inefficiency in the design of various water structures such as spillways and storm water drainage systems that are based on high return period events.

Drying model based on the relative humidity profile of thin-layer tomatoes in an indirect solar dryer

This work defined the development of a model for thin-film drying of tomatoes using an indi-rect solar dryer. Drying experiments were carried out and the drying model was approximated by a simplified model determined by the measurements collected during the experiments, in particular, the measurement of the relative humidity of the tomato during two days of drying coupled with the thermal behaviour of the drying device in free convection. The results show that with good measurements, it is possible to approximate the drying characteristic curve by a linear model with very high statistical performance indicators. The experiments also show that depending on the drying process adopted, the water behaviour of the tomato can change. In addition, the thin-film drying model adopted made it possible to assess the solar drying kinetics of the tomato variety studied. The results obtained finally show that the dryness of the tomato is reached after about 14 hours of drying. The drying temperature reaches an average of 80°C, and the final product water content after the optimal drying time is about 0.40 kg.water/kg.ms on a dry basis. At the end of this study, we concluded that the drying air temperature represents the most important parameter affecting the drying kinetics. The very good agreement between the experimental and numerical results obtained shows that the theoretical model and assumptions used are acceptable, and that our calculation model is reliable.