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Integrating GOF Tests and Cross Validation for Copula Model Selection.
(Science Direct, 2026) Otieno, K.; Chaba, L.; Omondi, E.; Odhiambo, C.; Omolo, B.
In dependence modeling, choosing the right copula is crucial, as different copula models can yield distinct interpretations of the relationship between variables. However, real-world applications are often constrained by the limitations of existing copula selection methods, which lack consistency and robustness across datasets. The selection methods in the literature that includes goodness-of-fit (GoF) tests and selection criteria, often yield conflicting results, thereby misrepresenting the dependence structure and leading to misleading conclusions. This study developed an integrated copula selection framework that combines GOF tests with cross-validation techniques. We integrated block-based cross-validation with GoF tests, where data was partitioned into blocks of different sizes. A copula was fitted on the training set, and its performance was validated on the test set using GoF measures. The selection process was repeated across multiple folds, and an aggregation method was applied to determine the most suitable copula. The approach was tested through Monte Carlo simulations and an empirical study was tested on weather variables in Kenya. The findings show that Kendall-based Kolmogorov Smirnov (KendallKS) and Cramrvon Mises (KendallCvM) test statistics integrated with stratified cross-validation, when, perform better when the Benjamini Hochberg (BH) procedure was used for aggregation. The proposed tests successfully identified the true copula and consistently rejected incorrect alternatives, with performance improving as sample size and dependence level increased. The empirical application further demonstrates the methods robustness in real-world settings. These findings demonstrate that the proposed approach enhances the reliability and stability of copula selection.
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Hybrid Deep Learning for Anti-Money Laundering: Unsupervised Detection of Emerging Schemes Via Feature Fusion and Explainable Artificial Intelligence, Machine Learning with Application
(Science Direct, 2026) Kungu, C.O.; Senagi, K.; Omondi, E.
Traditional rule-based anti-money laundering (AML) transaction monitoring systems suffer from high false-positive rates and rigidity in detecting complex emerging risk. This limitation has prompted changes to the Financial Action Task Force (FATF) recommendation 16, mandating the use of advanced systems for detecting money laundering schemes in cross-border payments. This study developed a hybrid framework integrating VAE-learned behavioural latent factors, GNN-captured relational network signals, and rule-based heuristics for enhanced anomaly detection. The model was evaluated on 54,258 real-world cross-border transaction records from an East African commercial bank. The One-Class SVM, optimised via a rigorous grid search proved superior compared to Isolation Forest and Local Outlier Factor benchmark, achieving a precision of 99.63% in the top 5% of prioritised alerts. Independent validation by a Kenyan financial institution confirms a batch processing speed of 1000 transactions per second on standard computer hardware (Intel Core i7, 16 GB RAM) and efficient high-priority alert triage, key requirements for deployment in financial institutions. Shapley additive explanations analysis further provided the interpretability of the feature contribution to the model performance. These results demonstrated that integration of rule-based features with deep-learning embeddings improves compliance work efficiency and proven pathway for resource-constrained financial institutions to comply with FATF regulatory demands upcoming in 2030.
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Comparing Allometric Models to Machine Learning Models for Aboveground Biomass Estimation in Agroforestry Systems in Kenya, Machine Learning with Applications
(Science Direct, 2026) Kigotho, S. I.; Senagi, K.; Olukuru, J.; Makori, D.M.; Abdel-Rahman, E.M.; Omondi, E.
This study compared traditional allometric models with machine learning (ML) techniques for accurately estimating aboveground biomass (AGB) in six Acacia species within Kenyan agroforestry systems. Using tree diameter at breast height (DBH) and total height as inputs, the research evaluated allometric models (Chave’s, Brown’s, and Henry’s) against ML models, including Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR). This research advances the field by benchmarking machine learning and classical allometric models at the species level within Kenyan agroforestry systems and employing SHapley Additive exPlanations (SHAP) for interpretability analysis. Model performance was validated using cross-validation, and predictive accuracy was assessed using the coefficient of determination ( ), Root Mean Squared Error (RMSE), relative Root Mean Squared Error (rRMSE), and Mean Absolute Percentage Error (MAPE). The results demonstrated that ML models generally outperformed the allometric approaches. While Chave’s allometric model was the best-performing traditional method, ML models such as GB and XGBoost achieved superior accuracy for most species, as reflected by higher predictive accuracy (R2) ) and lower errors as measured by RMSE, rRMSE, and MAPE. For example, GB performed best for Acacia drepanolobium (R2 = 0.989) ), while XGBoost showed the highest accuracy for Acacia nilotica (R2 = 0.990). RF also demonstrated strong and stable performance, whereas SVR exhibited comparatively lower and less consistent accuracy across species. SHAP value analysis indicated that DBH was the most influential predictor across all ML models, with tree height providing complementary explanatory power. The findings highlight the superior adaptability of ML models in capturing complex, non-linear relationships in heterogeneous agroforestry environments. This study contributes empirical evidence supporting the integration of ML techniques with conventional allometric approaches as a robust framework for improving AGB estimation. Future research should incorporate additional predictors such as wood density and integrate remote sensing data such as Light Detection and Ranging (LiDAR) to enhance scalability and precision, thereby supporting improved carbon stock assessments and agroforestry-based carbon credit initiatives.
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Extreme Weather Events and Pathways to Mental Health Outcomes in Sub-Saharan Africa: A Systematic Review,Climate Risk Management
(Science Direct, 2026) Wambua, G. N.; Corvetto, J. F.; Wekesah, F.M.; Bunker, A.; Mthiyane, N.; Muanido, A.; Cumbe, V.; Omondi, E.; Hunt, X.; Iwuji, C
Climate change has led to more frequent and intense extreme weather events (EWEs) globally, with sub-Saharan Africa (SSA) being disproportionately affected. Given the region's socioeconomic vulnerability and strong reliance on agriculture for subsistence, EWEs have the potential to affect the local population's mental health. The present systematic review synthesizes evidence on the impacts of EWEs on mental health in SSA, examining moderators and the pathways by which EWEs affect mental health, and identifying particularly vulnerable populations. In accordance with the PRISMA guidelines, we searched the PubMed, PsycINFO, and Web of Science databases and grey literature sources for relevant publications up to June 2024. Of the 3242 initially identified articles, 15 peer-reviewed journal articles from seven countries met the inclusion criteria. Studies examined floods (n=8), droughts (n=7), and heavy rainfall (n=1) as EWEs, and reported psychological distress, anxiety, depression, and post-traumatic stress disorder (PTSD) as mental health outcomes. Findings indicate that EWEs affect mental health through both direct pathways, reflecting immediate psychological trauma from exposure, and indirect pathways, operating through displacement, economic instability, and water and food insecurity. Moderators included protective factors such as social support and religion, as well as risk factors such as being an adolescent, being female, and being economically disadvantaged. There were few longitudinal studies, a limited examination of heatwaves, and a lack of culturally sensitive strategies for mental health support, indicating evidence gaps. As the incidence of EWEs increases across SSA, there is an urgent need for expanded research, improved health systems, and targeted interventions for vulnerable populations.
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Modelling Correlates of Adolescent and Young People Viral Suppression Pathway in Zambia: A Bayesian Multi-State Approach.
(Taylor &Francis, 2026) Fwemba, I.; Iddi, S.; Alfred, Y.; Guure, C.; Tamuzi, J.; Nyasulu, P. S.; Bosomprah, S.
This study aimed to look into the correlates of viral suppression in HIV-infected adolescents by utilizing Markov renewal multi-state survival models based on the Bayesian Approach, which use a deterministic posterior approximation. We developed a patient-level viral suppression transition framework to quantify the prognostic factors influencing viral suppression. The results showed that HIV-infected adolescents had higher risk of attaining viral suppression relative to remaining engaged, transfer out, being inactive, and dying among patients with VL < 50 compared to those with <100,000 was low. Older adolescents living with HIV were significantly less likely to retain to care and significantly less likely to attain viral suppression relative to those between 10 and 14 years. Those who retained in care, aged between 15-19 and 20-24 years were 9 and 8% more likely to attain viral suppression relative to those who were younger. While adolescents living with HIV attaining viral suppression were 15% more likely to transfer out of care and were 13% less likely to die. The proposed modeling approach identifies the transition rates of moving through each stage in the care cascade. The findings presented have appropriate policy implications and can be helpful in guiding implementation of targeted adolescent specific programs.