Conventional Regression Research Articles

Discerning the influence of climate variability modes, regional weather features and time series persistence on streamflow using Bayesian networks and multiple linear regression

AbstractA large literature on the sensitivity of streamflow response to climate variations has emerged over the past several decades, but the underlying mechanisms are not fully understood. The usual approaches to this problem have been simple and do not fully address its complexity, which involves the individual and joint effects of large‐scale climate modes and smaller‐scale weather features on streamflow volumes, the presence of persistence in these time series and their effects on antecedent conditions. Ongoing improvements in observational and reanalysis datasets and online access allow for better quantification of the connections between streamflow response, climate variability modes and regional weather features. The purpose of this paper is to determine whether Bayesian networks can be used to better identify key factors and their associated pathways leading to streamflow generation. A Markov blanket approach is described and illustrated using monthly streamflow series recorded at eight gauging stations within or immediately adjacent to the coastal region of eastern mainland Australia. The method is compared and contrasted with conventional multiple linear regression with discussion around this type of approach as part of a growing interest in machine learning applications. One example is used to illustrate the application of both approaches to a streamflow series exhibiting strong non‐stationarity. Results for the other streamflow series are included in a concise synthesis. Overall, the results suggest that Bayesian networks have several desirable features in terms of transparency, interpretability and explanatory insight. The findings from this study lend support to the use of Bayesian networks for modelling connections between streamflow volume and the variability of climate and regional weather. This improved understanding of the key controls of streamflow variability is intended to help address growing needs around informing social, cultural, economic and ecological aspects of water planning and management.

Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations

Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients.

Design of a soft sensor based on silver-coated polyamide threads and stress-strain modeling via Gaussian processes

The demand for reliable and efficient soft sensors has grown exponentially with the evolution of wearable devices and smart textiles. However, existing soft sensors often face challenges related to hysteresis, noise, and accuracy, hindering their seamless integration into practical applications. Our research presents a pioneering stress–strain soft sensor model based on silver-coated polyamide threads, augmented with additional silicone and graphite coatings for enhanced properties. Specifically, the silicone coating proves instrumental in elevating the sensor’s gauge factor and reducing noise, resulting in heightened accuracy. The extensive data analysis reveals the presence of hysteresis and non-linearities; however, our data exhibits remarkable robustness, as indicated by the high Spearman correlation coefficient values. In the context of system identification, a comparative analysis between traditional regression methods and Gaussian Process (GPs) Regression demonstrates the superior performance of GPs: this technique outperforms conventional regression techniques, obtaining 8.75 ± 4.06% Root Mean Square Error (RMSE) compared to the 12.70 ± 7.04% error observed in traditional methods. This research not only advances the field of soft sensor technology by developing an accurate, affordable and adaptable device, but also offers valuable insights into highly effective system identification techniques tailored for wearable devices.

Development and validation of a stacking ensemble model for death prediction in the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

ObjectiveThis study aimed to develop and validate a mortality risk prediction model for older people based on the Chinese Longitudinal Healthy Longevity Survey using the stacking ensemble strategy. Material and methodsA total of 12,769 participants aged 65 or more at baseline were included. Ensemble machine learning models were applied to develop a mortality prediction model. We selected three base learners, including logistic regression, eXtreme Gradient Boosting, and Categorical + Boosting, and used logistic regression as the meta-learner. The primary outcome was five-year survival. Variable importance was evaluated by the SHapley Additive exPlanations method. ResultsThe mean age at baseline was 88, and 57.8 % of participants were women. The CatBoost model performed the best among the three base learners, the area under the receiver operating characteristics curve (AUC) reached 0.8469 (95%CI: 0.8345–0.8593), and the stacking ensemble model further improved the discrimination ability (AUC = 0.8486, 95%CI: 0.8367–0.8612, P = 0.046). Conventional logistic regression had comparable performance (AUC = 0.8470, 95 % CI: 0.8346–0.8595). Older age, higher scores for self-care activities of daily living, being male, higher objective physical performance capacity scores, not undertaking housework, and lower scores on the Mini-Mental State Examination contributed to higher risk. ConclusionsWe successfully constructed and validated a few death risk prediction models for a Chinese population of older adults. While the stacking ensemble approach had the best prediction performance, the improvement over conventional logistic regression was insubstantial.

On a class of linear regression methods

In this paper, a unified study is presented for the design and analysis of a broad class of linear regression methods. The proposed general framework includes the conventional linear regression methods (such as the least squares regression and the Ridge regression) and some new regression methods (e.g. the Landweber regression and Showalter regression), which have recently been introduced in the fields of optimization and inverse problems. The strong consistency, the reduced mean squared error, the asymptotic Gaussian property, and the best worst case error of this class of linear regression methods are investigated. Various numerical experiments are performed to demonstrate the consistency and efficiency of the proposed class of methods for linear regression.

Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury

AimThis study aims to develop prediction models for in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury (TBI). MethodWe conducted a retrospective review of patients hospitalized for moderate-to-severe TBI in our department from 2011 to 2020. Five machine learning (ML) algorithms and the conventional logistic regression (LR) model were employed to predict in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes. These models utilized clinical and routine blood data collected upon admission. ResultsThis study included a total of 196 patients who received only non-surgical treatment after moderate-to-severe TBI. When predicting mortality, ML models achieved area under the curve (AUC) values of 0.921 to 0.994 using clinical and routine blood data, and 0.877 to 0.982 using only clinical data. In comparison, LR models yielded AUCs of 0.762 and 0.730 respectively. When predicting the GOS outcome, ML models achieved AUCs of 0.870 to 0.915 using clinical and routine blood data, and 0.858 to 0.927 using only clinical data. In comparison, the LR model yielded AUCs of 0.798 and 0.787 respectively. Repeated internal validation showed that the contributions of routine blood data for prediction models may depend on different prediction algorithms and different outcome measurements. ConclusionThe study reported ML-based prediction models that provided rapid and accurate predictions on short-term outcomes after non-surgical treatment among patients with moderate-to-severe TBI. The study also highlighted the superiority of ML models over conventional LR models and proposed the complex contributions of routine blood data in such predictions.

Identifying Hidden Factors Associated with Household Emergency Fund Holdings: A Machine Learning Application

This paper describes the results from a study designed to illustrate the use of machine learning analytical techniques from a household consumer perspective. The outcome of interest in this study is a household’s degree of financial preparedness as indicated by the presence of an emergency fund. In this study, six machine learning algorithms were evaluated and then compared to predictions made using a conventional regression technique. The selected ML algorithms showed better prediction performance. Among the six ML algorithms, Gradient Boosting, kNN, and SVM were found to provide the most robust degree of prediction and classification. This paper contributes to the methodological literature in consumer studies as it relates to household financial behavior by showing that when prediction is the main purpose of a study, machine learning techniques provide detailed yet nuanced insights into behavior beyond traditional analytic methods.

Impacts of spatial imputation on location-allocation problem solutions

Georeferenced data often contain missing values, and such missing values can considerably affect spatial modeling. A spatial location model can also suffer from this issue when there are missing values in its geographic distribution of weights. Although general imputation approaches have been developed, one distinguishing fact here is that spatial imputation generally performs better for georeferenced data because it can reflect a fundamental property of those data, that is, spatial autocorrelation or spatial dependency. This paper explores how spatial imputation exploiting spatial autocorrelation can contribute to estimating missing values in a weights surface for location modeling and subsequently improve solutions for spatial optimization, specifically p-median problems using a spatially imputed weights surface. This paper examines two spatial imputation methods, ordinary co-kriging and Moran eigenvector spatial filtering. Their results are compared with conventional linear regression, essentially Expectation-Maximization algorithm results for independent observations of Gaussian random variable cases. Simulation experiments show that spatial imputation produces better results for georeferenced data than simply ignoring any missing values and non-spatial imputation, and appropriately imputed values can enhance spatial optimization solutions, regardless of the number of medians, p.

Identifying nonlinear effects of factors on hit-and-run crashes using interpretable machine learning techniques

Previous research on hit-and-run crashes employed regression methods or machine learning techniques. However, regression methods necessitate preestablished model formulations, making it challenging to accommodate intricate nonlinear effects. In contrast, machine learning methods are characterized as black box systems, lacking interpretability. Thus, we propose an innovative analytical framework that combines data-driven machine learning algorithms with emerging interpretation techniques. The complex nonlinear effects of various factors on hit-and-run crashes are investigated by employing post hoc interpretation techniques, specifically, Shapley Additive exPlanations and accumulated local effect. The results demonstrate that machine learning algorithms are superior in accounting for complex relationships among influencing factors and identifying hit-and-run crashes. The quantitative importance of various factors is estimated and compared to reveal key determinants such as visibility, road location, and accident liability. The complex effects of different factors on hit-and-run crashes are unveiled, delineating quantitative piecewise nonlinear patterns. These patterns, which are difficult to capture using conventional regression models with predefined formulations, shed light on the nuanced dynamics of hit-and-run crashes. This research offers quantitative analysis and data-supported insights for transportation agencies and police departments to proactively mitigate hit-and-run crashes.

A recall-optimised machine learning framework for small data improves risk stratification for Hirschsprung's disease

ObjectiveTo improve the selection of patients with suspected Hirschsprung's disease for rectal biopsy with a recall-optimised machine learning framework that strongly penalises missed diagnoses, identify the most important clinical features, and investigate if this methodology is superior to the current practice of conventional logistic regression analysis. Study designThe study data comprised the medical records of 178 children older than one month who underwent a rectal biopsy for the evaluation of Hirschsprung's disease. Each medical record contained the clinical features recorded before the rectal biopsy and the biopsy result. However, only 20 of the 178 children were diagnosed with Hirschsprung's disease. Thus, as is frequently the case for rare diseases, the dataset was small and imbalanced. We present a machine learning framework that, based on these data, produces a champion machine learning model for predicting the presence of Hirschsprung's disease in patients. Notably, the machine learning framework is recall-optimised, i.e., it strongly penalises missed diagnoses. This is achieved using a combination of synthetic minority over-sampling techniques, Bayesian hyper-parameter optimisation, and bootstrapping-based prediction thresholding for a competing set of models comprised of a logistic regression model, a random forest classifier, and a gradient-boosted classifier. Finally, the machine learning framework evaluates the performance of these three competing models and conventional logistic regression with 5-fold stratified cross-validation using an 80:20 train-test split. Model performance is ranked by the average recall across the cross-validation folds. The model with the highest recall is selected as the champion. The model with the highest accuracy is chosen in case of a tie. ResultsThis study revealed that about 35% of our cohort's children without Hirschsprung's disease could have been spared a rectal biopsy while incurring no missed diagnoses using our machine learning framework. Given our aim of avoiding missed diagnosis, the champion model was vastly superior to conventional logistic regression, i.e., the status quo, which missed 40% of HD-positive cases. Moreover, the champion model pointed to a new hierarchy for the importance of clinical features associated with Hirschsprung's disease. Among all features, “gross abdominal distention” was the most important. ConclusionThere is considerable scope for a stricter selection when referring constipated children for a rectal biopsy to diagnose or exclude Hirschsprung's disease. This study demonstrates that a recall-optimised pipeline based on classical supervised machine learning is superior to the conventional statistical and heuristic approaches used today, also for small and imbalanced datasets. This finding opens a path to better care for patients with rare diseases while alleviating pressures on healthcare systems.

Prediction of Major Adverse Cardiac Events After Transcatheter Aortic Valve Implantation: A Machine Learning Approach with GRACE Score.

Predictive risk scores have a significant impact on patient selection and assessing the likelihood of complications following interventions in patients with severe aortic stenosis (AS). This study aims to explore the utility of machine learning (ML) techniques in predicting 30-day major adverse cardiac events (MACE) by analyzing parameters, including the Global Registry of Acute Coronary Events (GRACE) score. This retrospective, multi-center, observational study enrolled 453 consecutive patients diagnosed with severe AS who underwent transcatheter aortic valve implantation (TAVI) from April 2020 to January 2023. The primary outcome was defined as a composition of MACE comprising periprocedural myocardial infarction (MI), cerebrovascular events (CVE), and all-cause mortality during the 1-month follow-up period after the procedure. Conventional binomial logistic regression and ML models were utilized and compared for prediction purposes. The study population had a mean age of 76.1, with 40.8% being male. The primary endpoint was observed in 7.5% of cases. Among the individual components of the primary endpoint, the rates of all-cause mortality, MI, and CVE were reported as 4.2%, 2.4%, and 1.9%, respectively. The ML-based Extreme Gradient Boosting (XGBoost) model with the GRACE score demonstrated superior discriminative performance in predicting the primary endpoint, compared to both the ML model without the GRACE score and the conventional regression model [Area Under the Curve (AUC)= 0.98 (0.91-0.99), AUC= 0,87 (0.80-0.98), AUC= 0.84 (0.79-0.96)]. ML techniques hold the potential to enhance outcomes in clinical practice, especially when utilized alongside established clinical tools such as the GRACE score.

Mangrove above-ground carbon estimation from Sentinel-1A (SAR) and field-based data in Tien Yen district, Quang Ninh province

Mangrove forests have been globally recognized as they play a vital role in preventing coastal erosion, mitigating the effects of wave actions, and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. Sentinel-1 (SAR) offers a new opportunity for mangrove cover mapping and biomass estimation, especially in the tropics where mangrove deforestation and degradation are highest and cloud cover is persistent. This study used the Sentinel-1A-derived VV/VH polarizations for mangrove cover mapping with thresholds of -23.5<VH<-9.05 and -17.5<VV<-3.8. Upon using VV and VH polarizations for mangrove cover mapping compared to PlanetScope data, it has been confirmed that these polarizations are suitable for mangrove cover monitoring along the coast of Tien Yen with an overall accuracy of over 90.5% and Kappa coefficient greater than 0.78 in 2022. This study also developed mangrove AGB models based on the field survey data and SAR data for estimating the AGB of mangrove forests in Tien Yen. In fact, we evaluated the capability of using Sentinel-1A for the retrieval and predictive mapping and mangrove AGB through the conventional linear regression models. The findings show that the models based on VV and VH polarization values from Sentinel-1A can be used for mangrove AGB estimation. Overall, selected AGB Model 1 with R2=0.445 (p-value<0.001) has provided an option for carbon estimation. To have more accurate AGB models based on SAR data, this study also suggests that more research should be carried out using more advanced machine learning models based on Sentinel-1A and Sentinel-1B for carbon estimation of mangrove forests in Tien Yen.

Machine learning algorithms to study the relative roles of jet convection and natural convection in the presence of surface roughness elements on enhancement of jet impingement heat transfer

Based on data-driven approaches, eight machine-learning algorithms have been proposed to characterize the heat transfer performance of turbulent jet impingement cooling. These algorithms are designed to cool high-heat-flux surfaces modified with single protrusions, multi-protrusions, or V-grooves. To train these algorithms, an experimental dataset was used, which included 184 experiments with Reynolds numbers ranging from 10,000 to 33,000, nozzle exit-to-plate distances ranging from 2 to 10 jet diameters, and two different heat inputs of 60 W and 90 W. Multiple linear regression algorithms in Python programming were used to model jet impingement cooling, showing that the proposed machine learning models provided more accurate predictions with a 20.4 % to 66.5 % relative residual improvement over the existing conventional regression model based on performance metrics like R2 and relative residual. This improvement was leveraged to quantify natural convection's impact on the effectiveness of heat transfer enhancement due to different types and sizes of roughness elements in high-temperature applications. The research found that as the protrusion surface area increased from 0.02 % to 0.188 % compared to the flat plate, the effectiveness of heat transfer enhancement decreased from 2.69 % to 8.37 % at similar operating conditions. These results revealed that natural convection, which opposes forced jet impingement, was strengthened with increased heat input values, roughness element surface area, and the local turbulence generated. The higher natural convection weakened the enhancement afforded by jet impingement on the modified plates relative to the flat plate. Additionally, the capabilities of machine learning algorithms were explored to unlock the full potential of the jet impingement technique and to overcome the associated complexity in jet impingement applications for next-generation electronics cooling. Finally, the Decision Tree Classifier algorithm was applied to classify the performance of the surface roughness element as either effective or ineffective.

PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS.

Ignoring measurement errors in conventional regression analyses can lead to biased estimation and inference results. Reducing such bias is challenging when the error-prone covariate is a functional curve. In this paper, we propose a new corrected loss function for a partially functional linear quantile model with function-valued measurement errors. We establish the asymptotic properties of both the functional coefficient and the parametric coefficient estimators. We also demonstrate the finite-sample performance of the proposed method using simulation studies, and illustrate its advantages by applying it to data from a children obesity study.

Multi-step solar radiation prediction using transformer: A case study from solar radiation data in Tokyo

The widespread advancement of computer technology resulted in the increasing usage of deep learning models for predicting solar radiation. Numerous studies have been conducted to explore their research potential. Nevertheless, the application of deep learning models in optimizing building energy systems, particularly in a multi-step solar radiation prediction model for model predictive control (MPC), remains a challenging task. This is mainly due to the intricacy of the time series and the possibility of accumulating errors in multistep forecasts. In this study, we propose the development of a transformer-based attention model for predicting multi-step solar irradiation at least 24 h in advance. The model is trained and tested using measured solar irradiation data and temperature forecast data obtained from the Tokyo Meteorological Agency. The findings indicate that the transformer model has the capability to effectively mitigate the issue of error accumulation. Additionally, the generative model exhibits a significant improvement in accuracy, with a 62.35% increase when compared to the conventional regression LSTM model. Additionally, the transformer model has been shown to attain superior prediction stability, mitigate the effects of error accumulation in multi-step forecasting, and circumvent training challenges stemming from gradient propagation issues that can occur with recurrent neural networks.

Optimization of Fuzzy Regression Transfer Learning using Genetic Algorithm for Cross-Domain Mapping

Artificial intelligence and big data have become widely utilized in industry and thus machine learning has been extensively researched. However, it is challenging to apply existing data-driven methods when the amount of data is insufficient. Therefore, transfer learning, which reuses knowledge acquired from domains with similar data characteristics and tasks, has gained attention for achieving fast and accurate model learning in new domains. Although numerous transfer learning methods have been proposed for classification problems, few have been proposed for regression problems. Moreover, conventional fuzzy regression transfer learning tends to work well only in limited domain environments with extremely limited target data, making its application to real-world data challenging. The present study applies a combination of regression models based on Takagi-Sugeno fuzzy theory and transfers learning to regression problems in domains with incomplete knowledge. We propose two methods, one based on a genetic algorithm and one based on differential evolution combined with a genetic algorithm, for optimizing mapping for input space modification and applying them to real datasets. The results of evaluation experiments demonstrate that the proposed methods have higher efficiency and learning accuracy than those of conventional methods.

Proof‐of‐concept of a data‐driven approach to estimate the associations of comorbid mental and physical disorders with global health‐related disability

AbstractObjectiveThe standard method of generating disorder‐specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities.MethodsWe propose an alternative, data‐driven, method of generating disorder‐specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self‐reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder‐specific) disability assessed by clinician ratings or by survey respondent self‐reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder‐specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645).ResultsAdjustments for comorbidity decreased estimates of disorder‐specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant.ConclusionsThe results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder‐specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity.

USING MULTI-LEVEL MODELING TO EXAMINE RACIAL DISPARITIES IN PALLIATIVE CARE AMONG OLDER ADULTS WITH HEART FAILURE

Abstract Studies have found persistent disparities in guideline-concordant cares for heart failure among patients who self-identify as Black. We investigated how the guideline-concordant specialist palliative care (SPC) varied with race among older adults with advanced heart failure (AHF). We identified 16,464 older adults aged ≥65y (median: 74y) with AHF by ICD-9/10 codes seen between 1/1/2018-7/1/2020 in 83 Veterans Affairs Medical Centers (VAMC). In conventional logistic regression without accounting for between-VAMC variation, Black (28%) race was less likely to receive SPC services than White (70%) race [adjusted odds ratio (aOR) of 0.91 (95% Confidence Intervals (CI): 0.84,0.99); predicted probability: 27.6% vs. 29.5%], independent of age, sex and comorbidity. This association diminished to null [aOR: 0.99 (95% CI: 0.90,1.08)] after adding a random intercept for VAMCs (multi-level model (MLM)). Expanding the MLM with specific VAMC characteristics (SPC team staffing (p< 0.05), facility complexity and urbanicity) as fixed effects improved overall model fit (area under ROC: 0.68 vs 0.61, p< 0.05), and accounted for 17% of the VAMC-level variance (intraclass correlation coefficient: 0.069 vs 0.083). There were no cross-level interactions between race and VAMC characteristics. These findings suggest that variation in the individual probability of receiving SPC between Black and White race in this older AHF cohort may be partially explained by between-VAMC differences in SPC staffing and other facility characteristics. To promote equitable access to SPC for older adults with AHF, further research is needed to examine service delivery characteristics and broader contextual determinants of health, such as area socioeconomic disparity of healthcare facilities

Validation of Growth Rate Estimate Using Spline Regression Technique for Green Gram Production in Odisha

The varying pattern of agriculture data in different phases over a long period could be thought of capturing effectively by the spline regression technique. The present study focuses on using spline regression in estimating the growth rate of production of green gram for Odisha. This technique requires the segmentation of the entire study period into distinct periods by inserting knots at suitable intervals. It ensures the same pattern of variation in a particular segment and an abrupt change in the pattern of variation in the next segment. The spline regression approach was compared to the conventional regression technique based on specific statistical criteria to confirm its performance. To estimate the growth rate of green gram in Odisha, spline regression models outperformed conventional regression models, that is why they were chosen for the first and last segments of the study period for area, yield, and output.

An artificial neural network for predicting air traffic demand based on socio-economic parameters

Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress. Accurately forecasting the future demand for air travel is crucial for making long-term commitments to these economies. The findings of this study provide valuable insights into the provision of low-cost passenger carrier demand. They can assist low-cost carriers, airports, airport consultants, and government agencies in their strategic planning and development processes. The primary objective of this study is to identify and evaluate artificial intelligence-based solutions, specifically, Artificial Neural Networks (ANN), to determine the most effective model for projecting domestic demand for low-cost carriers. The study analyzes nine variables that impact the accuracy of predicting domestic air passenger demand in emerging countries using a Multilayer Feed-Forward Backpropagation Neural (MFFBPN) network. The performance of the developed ANN model for predicting air passenger demand is assessed through training, validation, and testing using 240 data sets. The evaluation metrics employed, including mean square error (MSE), root mean square error (RMSE), accuracy, precision, sensitivity, and specificity, indicate the effectiveness of the developed ANN model, with results of 0.0019 for MSE, 0.0440 for RMSE, 0.92 for accuracy, 0.92 for precision, 0.93 for sensitivity, and 0.95 for specificity. We show that modeling based on artificial intelligence approaches and ANN is more effective than conventional linear regression models.