Modeling Earth Systems and Environment

The automobile industry in Pakistan is affected by economic crises, leading to a surge in old vehicle imports. This study assessed exposure to cadmium (Cd), chromium (Cr), lead (Pb), and nickel (Ni) in paints and dust samples from auto workshops. A total of 56 samples of four dust and paint flakes from each of the 14 workshops were collected based on high traffic, long operational history, and dense population areas in Faisalabad. Heavy metal concentrations were analyzed using an atomic absorption spectrophotometer. Results revealed that the maximum concentrations in dust samples were 3.11, 78, 46, and 31 mg kg−1 for Cd, Cr, Pb, and Ni, respectively, while in paint samples, they were 10, 7.4, 93, and 18 mg kg−1. Positive matrix factorization (PMF) model attributed metal sources to human activity and corrosion of aged vehicular components. The geo-accumulation index was maximum for Pb at W13, Cd and Cr at W10, and Ni at W9 while the Cr, Pb, and Ni had moderate contamination factors (CF), whereas Cd had CF of high pollution. Workshops W11 and W14 recorded pollution load index > 1, showing contamination. Ecological risk was low, with the exceptions of W2 and W12 (moderate risk), and potential ecological risk was high. For adults and kids, the total cancer risk (TCR < 1) and non-carcinogenic health hazard (HI < 1) were low. The study recommends implementing regulations to control risks and training workers to reduce exposure to contaminated dust and paints.

This study presents a mathematical model incorporating the effects of memory on the spread of Q fever, considering humans, animals, and environmental contamination of Coxiella burnetii. The model is formulated using a system of fractional-order differential equations with the Caputo fractional derivative to capture the long-term impact of past infections and environmental contamination. A mathematical analysis is performed, including positivity and boundedness of solutions, ensuring biological feasibility. The existence and uniqueness of solutions are established to confirm the model's well-posedness. Stability analysis is conducted using Hyers-Ulam stability analysis, which guarantees that small perturbations in initial conditions do not lead to significant deviations in the system's behavior. Sensitivity analysis is carried out to determine the most influential parameters affecting disease transmission, and a sensitivity heat map analysis is used to visualize their impact. From the sensitivity heat map analysis, it is noticed that the model parameters are more sensitive to the susceptible humans, S_h; recovered humans, R_h ; susceptible cattle, S_c ; recovered cattle, R_c ; and the bacteria in the environment. Numerical simulations further demonstrate the role of memory in prolonging disease persistence, highlighting that ignoring historical effects may underestimate the long-term risks of Q fever outbreaks with consideration of human, animal, and environmental contamination.

In this study, a mineral-organic composite adsorbent (Montm@GHBC) was synthesized using Montmorillonite clay and biochar derived from Gossypium hirsutum L., the world's most widely cultivated cotton species. The composite was prepared via an impregnation-carbonization method, and its adsorptive properties were evaluated for the removal of Malachite Green (MG) dye from aqueous solutions. The fabricated adsorbent was characterized using X-ray diffraction, Brunauer–Emmett–Teller (BET) analysis, scanning electron microscopy, and Fourier transform infrared spectroscopy (FTIR). BET analysis revealed a specific surface area of 14.41 m²/g, primarily composed of mesopores, with a T-micro volume of 0.03 cm³/g and a BJH pore diameter of 61.11 Å. FTIR analysis identified functional groups such as –OH and –COOH, which are responsible for interactions between dye molecules and the composite. Under optimal conditions (natural pH, an adsorbent dose of 0.05 g, a contact time of 30 min, and room temperature), the maximum monolayer adsorption capacity was 110.7 mg/g, with an adsorption efficiency of 87.07%. Nonlinear regression analysis of the kinetic and equilibrium isotherm data indicated that the adsorption of MG onto the Montm@GHBC adsorbent follows a pseudo-first-order kinetic model and fits the Dubinin–Radushkevich isotherm model, suggesting a physical adsorption process. A thermodynamic study revealed that the adsorption process is exothermic and spontaneous. This study demonstrates that Gossypium hirsutum L. husk powder can be effectively valorized in combination with clay minerals to produce a cost-efficient and eco-friendly adsorbent material for the treatment of dye-contaminated water, specifically for the removal of MG. Graphical abstract

The desert locust (Schistocerca gregaria), a globally destructive pest, exhibits population dynamics shaped by environmental and climatic factors. This study introduces a non-autonomous compartmental model that integrates control strategies to explore the dynamics of biphasic locusts, focusing on interactions between seasonal rainfall and stage-specific interventions. The model incorporates a rainfall-driven carrying capacity, density-dependent phase transitions, and targeted controls across egg, hopper, band, and adult stages. Theoretical analyzes confirm the robustness of the model, showing that a locust-free equilibrium is locally and globally asymptotically stable when the control offspring number $\mathcal {N}_c < 1$, while non-trivial equilibria (swarm-free, solitarious-free or co-existence states) arise when $\mathcal {N}_c > 1$. The global sensitivity analysis of the autonomous model, combined with numerical simulations, reveals how control strategies and initial conditions influence long-term dynamics. Simulations indicate that $20\%-40\%$ control efficacy can reduce outbreaks to recession levels, preventing plague-scale infestations. Unlike previous models, this holistic framework captures the entire life cycle, offering a powerful tool for integrated locust management. These insights equip policymakers with actionable strategies to mitigate locust impacts.

This study proposes a geological hazards susceptibility evaluation method based on the spatial–temporal evolution of evaluation factors. The variation trend slope (Slope) is pre-processing a model to obtain the spatial–temporal evolution of evaluation factors. Then substitute the spatial–temporal evolution data of evaluation factors into ensemble models of information content method and logistic regression (ICM-LR), and information content method and artificial neural network (CIM-ANN), predicting the susceptibility of geological hazards, providing a reference for future prediction of geological hazards. In this study, the annual precipitation and normalized difference vegetation index (NDVI) of Jiuzhai gully from 2016 to 2021 are used as basic data to build Slope model. Train and validate using historical geological hazard data after the Mw 7.0 earthquake in 2017. Evaluate the model through curve area under curve (AUC) and compare it with previous research results. The study results indicate that the geological hazards susceptibility of ICM-LR and ICM-ANN obtained using the Slope model has high applicability. It can effectively improve the accuracy of short-term geological hazard prediction and provide a reference value for post-earthquake geological hazard prediction and post-earthquake reconstruction.

Developing a comprehensive understanding of reservoir architecture relies heavily on three-dimensional geological and hydrostratigraphic modeling. This study introduces the first 3D geological and hydrostratigraphic model of the Agourai Plateau, developed through the integration of diverse datasets, including borehole logs, geophysical surveys, geological maps, and structural network analyses. The model delineates the boundaries of geological formations to unravel the region’s complex geological framework. In which the integration of piezometric data into the solid model represents an improved approach compared to the conventional solid geological model. This integration enabled the generation of a detailed piezometric map that captures the inherent complexity of the karst system and facilitated the creation of high-resolution hydraulic cross-sections. The construction of the model was carried out using the Groundwater Modeling System (GMS) software, recognized for its advanced capabilities in three-dimensional subsurface modeling. This comprehensive model provides a refined visualization of the spatial distribution and variability of geological units, enabling a robust assessment of the aquifer’s storage capacity. The Agourai Plateau was classified into eight distinct geological units, starting with the foundational basement rock and extending to the overlying layers constituting the aquifer system. The total storage volume of the karst aquifer was estimated at 36.3 × 10⁹ m³, highlighting its significance as a regional water resource. Moreover, the model sheds light on spatial heterogeneities between geological units and elucidates the structural framework, including the distribution of major faults that critically influence aquifer dynamics. A comprehensive analysis of subsurface flow patterns was also conducted, offering valuable insights into the status of abandoned quarries and their implications for groundwater resources. This integrative approach provides a robust scientific foundation for the sustainable management and protection of water resources in vulnerable karst environments.

Meningitis is a major public health concern, especially in developing nations, due to its devastating consequences for human health. Although modeling studies have examined disease transmission dynamics, little attention has been paid to how control strategies affect the behavior of different population groups, including carriers, symptomatic individuals, hospitalized patients, and those in intensive care. This study proposes a computational framework that compares the effectiveness of vaccination of people at risk of the disease versus treating symptomatic infected persons. The basic reproduction number is used to evaluate the equilibrium points. Assess the precision of the proposed model’s illustration to data. We fit the meningitis model using the information at our disposal on meningitis cases reported in Nigeria from the first week of January to the last week of December 2023; this was obtained from the Nigerian Center for Disease Control (NCDC) database. We also performed a sensitivity analysis using a normalized forward sensitivity index to see which parameters had significant effects on the effective reproduction number. The results of both analytical techniques and numerical simulations reveal that recruitment rate, vaccination, progression from carrier to symptomatic stages, and disease-induced death all significantly reduce the incidence and prevalence of meningitis in the community. The study findings could be used to inform decisions about meningitis control initiatives.

This study presents an interactive AI-based crop and pest management system integrated into a mobile application, designed to enhance sustainable agricultural productivity through the use of advanced deep learning techniques. The proposed framework comprises two core modules: (i) Crop Recommendation and (ii) Pest Detection. The Crop Recommendation module utilizes a Convolutional Neural Network (CNN) built on transfer learning to process agronomic inputs such as macronutrient levels (Nitrogen, Phosphorus, Potassium), soil pH, and climatic parameters (Temperature, Humidity, Rainfall). This model is optimized with a learning rate of 0.0001, batch size of 16, and trained over 100 epochs. Following crop recommendation, the pest detection and recommendation module is executed using the IP102 dataset to ensure targeted pest identification and treatment. The Pest Detection module leverages EfficientNet B4, employing a learning rate of 0.001, batch size of 64, and trained for 11 epochs with ReLU and Softmax activations, a dropout rate of 0.4, and weight decay of 0.005. It processes high-resolution images (1024 × 1024 pixels) of rice crops annotated with disease types and severity to detect pest species and recommend targeted pesticide interventions. The system achieved a training accuracy of 96.32% and testing accuracy of 82.54% with EfficientNet B4. Deployed on an Android platform, the application delivers real-time, field-level decision support for pest identification and crop advisory, enabling precision farming and improved resource utilization in sustainable agricultural systems also contributes significantly to global sustainability goals.

Nile Delta is significantly affected by land subsidence and sea level rise due to anthropogenic activities and change global warming. In this study, the subsidence rates along the northeast (Port Said) and central parts (Tanta & Kafr Al-Zayat) of Nile Delta, Egypt were accurately investigated using the Small Baseline Subset (SBAS) technique and dense SAR data. A total of 26 Sentinel-1A satellite images covering the period from 2017 to 2021 were processed using a coherence threshold value of 0.4 and 75 ground control points (GCPs). The final estimated average subsidence velocity rates of Port Said and Tanta cities were about − 10 and − 25 mm/year, with total vertical displacement of − 27 mm and − 44 mm during the investigated periods, respectively. In general, the central part of the Nile Delta shows higher subsidence rates than the north eastern parts. In contrast to previous interpretations of land subsidence causes along the Nile Delta, which were controlled by natural processes, our findings show localized subsidence related to the ongoing vertical and horizontal urban expansions and groundwater extraction. In addition, the integrated effect of the sea level rise due to climate change as well as land subsidence was simulated to show which city (Port Said or Tanta) will be severely submerged due to such rise.

The Muskingum model is one of the most frequently used flood routing models. In any reach of a river with hydrometric stations at its beginning and end, model parameters can be determined based on floods measured at the inlet and outlet of this reach. Therefore, if another flood enters this reach, the outflow flood can also be predicted using the data of this flood and the Muskingum model parameters without cost. In this research, the type-1 non-linear Muskingum model (NLMM1), the flood data clustering method based on the k-means model, and the combination of NLMM1 with k-means (K-NLMM1) are used. This approach is utilized for four flood data sets, including the Wilson flood, the Wye River flood, the Lewis-Viessman flood and the Sutculer flood. Two optimization problems are defined in this paper. The first problem is to determine the center of clusters and the second one is to specify the parameters of NLMM1 and K-NLMM1. The bald eagle search (BES) optimization algorithm is used to solve such problems. The objective function for determining the Muskingum model parameters is considered to be minimizing the sum of squares of the difference between the observed outflow hydrograph and the routed hydrograph (SSQ). For the first to fourth case studies, the SSQ of the NLMM1 model is computed to be 245.6, 54,185.6, 74,307.2 and 557.3 (m³/s)², respectively. Also, the values of SSQ for the K6-NLMM1 superior model in the first to fourth case studies are computed to be 3.1, 1185.7, 40,427, and 115.3 (m³/s)², respectively. Thus, by clustering the data, the SSQ value decreases in the range of 46–99%.

In this study, we develop a fractional-order mathematical model to investigate the dynamics of mosquito populations with and without Wolbachia infection, focusing on its impact on dengue transmission and disease control. The model incorporates key biological factors, including reproduction, mortality rates, and Wolbachia transmission probabilities, to analyse the life cycle of eggs, larvae, pupae, and adult mosquitoes. Additionally, the framework is extended to a dengue transmission model, incorporating both Wolbachia-infected and non-infected adult mosquitoes to evaluate their role in disease spread and control. Using the Adams Predictor–Corrector numerical method, the interactions between mosquito populations and dengue transmission are simulated, providing insights into the effectiveness of Wolbachia-based interventions. Our findings highlight how Wolbachia-infected mosquitoes suppress dengue transmission by reducing the prevalence of infectious mosquitoes, offering a promising vector-control strategy. The results contribute to a deeper understanding of fractional-order modelling in epidemiology and reinforce the potential of Wolbachia deployment as an effective tool for managing mosquito-borne diseases.

Vietnamese Mekong Delta (VMD) experiences high soil loss and nutrient export, which in turn impacts food security and livelihood. These parameters play an important role in hydrological ecosystem services. Using the InVEST modeling software, we quantify and map sediment and nutrient regulation, as well as seasonal water yield, across the VMD. We propose an innovative approach for model validation, tailored to regions with limited data availability, that utilizes observed data to enhance the accuracy and reliability of the model. The results of this study indicate that approximately 65% of the total watershed is prone to high nutrient export, and 33% of the area is prone to soil erosion. Sensitivity analysis for the model parameters reveals that variations in k (nutrient delivery ratio, sediment delivery ratio) and β (seasonal water yield) have the most significant effects on sediment export and baseflow, respectively. This study focuses on regulating ecosystem services in agricultural regions that contribute to Vietnam’s food production. This study presents a series of implications and findings that contribute to the overall understanding of how hydrological ecosystem services can support food security in data-scarce regions, as well as offer recommendations to policymakers on the application of nature-based solutions and the sustainable management of water resources.

Rock Quality Designation (RQD) is a crucial parameter in rock mechanics and engineering, fundamental for the design and stability analysis of rock structures. Traditional methods for acquiring RQD data are often limited by high costs and restricted spatial coverage, underscoring the need for more efficient and accurate estimation techniques. This study introduces a novel approach by integrating the Gray Wolf Optimization (GWO) algorithm with the Inverse Distance Weighting (IDW) method, addressing the limitations of traditional IDW, such as its sensitivity to fixed parameters and its tendency to oversimplify complex geological variations. The proposed GWO-GEP method dynamically optimizes interpolation parameters, significantly enhancing the accuracy of RQD predictions. Utilizing data from 577 RQD observations across 15 boreholes in the Azad Dam area, the study compares the performance of the traditional IDW method with the GWO-GEP hybrid approach. The results show that while IDW provides satisfactory accuracy (with a coefficient of determination of 0.91 for the training set and 0.90 for the test set); the GWO-GEP method outperforms it with higher predictive accuracy (achieving a coefficient of determination of 0.99 for the training set and 0.98 for the test set). Additionally, the GWO-GEP model demonstrates lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, along with higher Nash–Sutcliffe efficiency (NSE) values, highlighting its superiority in handling spatial complexity and reducing prediction errors. These findings validate the effectiveness of the GWO-GEP method as a robust tool for RQD estimation, particularly in challenging geological settings. This study not only advances the methodology for RQD estimation but also emphasizes the practical benefits of applying intelligent optimization techniques in geotechnical engineering, ultimately contributing to more reliable and cost-effective engineering designs.

This study focus on the development of a mathematical model to analyze the impact of human activities on climate change by observing how climate events evolve over time. To investigate the various rates of climate change caused by human activity, a mathematical model has been developed based on hypotheses related to a healthy ecosystem. The Caputo operator is then used to transform the model into a fractional ordered model with theoretical solutions for ongoing monitoring. Also the next generation method is used to determine the models reproductive number to observe the climate change impact. Sensitivity analysis was developed to identify the most sensitive parameters and investigate the effects of variations in the rate of change under different circumstances. A suggested model is analyzed both qualitatively and quantitatively, with special attention paid to bounded-ness, positivity, existence and unique solutions. Both theoretically and statistically, the local stability of the model is verified. The Lyapunov derivative by endemic point of the model is used to investigate the global stability of the model. The effect of the fractional operator on the generalized form of the power law kernel for continuous monitoring of the climate change model due to human activities is investigated through numerical simulations using a two-step Lagrange polynomial method. The simulations demonstrate how various parameters impact occur on climate change due to human activities. The simulations are designed to replicate the behavior and impact of climate change due to human activities caused by both natural and human activities, and to observe the various measures for a healthy environment. Based on confirmed results for different strategies, this kind of research will be useful in determining how climate change impact occur due to human activities and in creating management plans.

Seasonal flu results from infection by influenza viruses of either type A or B. Common symptoms include a rapid rise in body temperature, coughing, headaches, muscle and joint aches, throat discomfort, and nasal congestion. This research addresses the need for effective modeling and control of seasonal influenza, which remains a significant health concern globally due to its high transmissibility and potential to cause severe illness. Current approaches to understanding and managing influenza focus on various mathematical models exploring transmission dynamics and control strategies. This study contributes to the field by introducing a Susceptible-Exposed-Infectious-Quarantined-Recovered (SEIQR) model, which uniquely incorporates quarantine as a key intervention, reflecting realistic disease management practices. The methodology utilized involves formulating the SEIQR model to simulate the transmission of influenza and analyze its stability. The stability of both the disease-free and endemic equilibrium points is examined using Lyapunov functions and LaSalle’s invariance principle, ensuring the rigorous validation of the model's behavior. To enhance the model's utility, optimal control theory is applied, incorporating control variables such as vaccination, social measures, and treatment for both infected and quarantined populations. The application of Pontryagin’s Maximum Principle enables the derivation of optimal control strategies that balance epidemiological impact with cost-effectiveness. Numerical simulations provide key results that demonstrate the efficacy of control interventions. Specifically, scenarios implementing control measures reveal a significant reduction in the peak and overall spread of infections. The analysis of different control policies indicates that a combined approach—employing both vaccination and social distancing—is the most effective for curbing the spread of influenza. Sensitivity analysis further underscores the critical influence of parameters like quarantine rate and infection rate on the basic reproduction number, R0, reinforcing the importance of targeted interventions. The study’s findings emphasize the importance of timely and multifaceted control measures for achieving the global asymptotic stability of the influenza model. The implications suggest that integrated strategies, particularly those involving vaccination and social controls, are crucial for public health policy to manage and prevent influenza outbreaks effectively. Future research could expand the model to include demographic variations, virus mutations, and interactions with other respiratory diseases, enhancing its predictive power and practical relevance for disease control.

Air pollution is one of the important problems of large cities today. This paper is devoted to the development of methods for predicting the concentration of fine particles PM2.5 in the atmosphere of Almaty. The main objective of the study is to evaluate the effectiveness of different neural architectures for predicting the concentration of PM2.5 in the air of Almaty. The study used data obtained from sensors installed at different points throughout the city. The paper focuses on recurrent neural networks and their modifications: LSTM (seq2vec), bidirectional LSTM (BiLSTM) and Seq2Seq for predicting the concentration of PM2.5. This allows us to compare the effectiveness of models depending on the window size. The results showed that LSTM is better at forecasting for 90 days, Seq2Seq—for 180 days, and BILSTM—for 365 days. The application of these models can improve the air quality monitoring and management system in cities.

Understanding spatiotemporal land-use and vegetation dynamics is vital for sustainable management; however, translating this knowledge into effective planning remains challenging. This study addresses the gap by assessing Land Use and Land Cover (LULC) dynamics in the Belabo-Diang Communal Forest (BDCF) in eastern Cameroon, employing remote sensing and Geographic Information System (GIS) techniques. Landsat images from December 1984, December 2002, and November 2020 were utilised for supervised classification using the maximum likelihood algorithm, resulting in thematic maps of land use/land cover (LULC). Post-classification analysis, conducted at the pixel scale and LULC process insights, assessed forest loss and change trajectories in the BDCF. Results show the BDCF covers 53,213.80 hectares, with seven LULC types: Mature Secondary Forest (MSF), Young Secondary Forest (YSF), Savannah (SA), Cropland and Fallow (CF), Bare Soil (BS), permanently flooded swamp (PFS), and periodically flooded swamp (TFS). MSF declined 37.78% (1984–2002) but increased 36.10% (2002–2020). The annual change per hectare shows that MSF decreased by 319.42 ha/year, while SA, CF, and BS increased by 401.12, 351.7, and 1281.4 ha/year, respectively, resulting in a loss of 19,625.93 ha in forest formations. These findings support BDCF managers in landscape restoration and the Reducing Emissions from Deforestation and Forest Degradation (REDD +) initiative, providing insights for sustainable land management, effective conservation strategies, and mitigating climate impact.

This study presents a novel framework for investigating soliton solutions to the generalized reaction Duffing equation, a key model in science and engineering that describes damped oscillators with complex potentials, capturing intricate behaviors. Its applications span electrical engineering, biomechanics, climate studies, earthquake research, and chaos theory, driven by its rich nonlinear dynamics. Using the Sardar Sub Equation and New Sub Equation methods, the study reveals a variety of exact traveling wave solutions, including hyperbolic, Jacobi, and trigonometric forms such as bell-shaped, kink, anti-kink, and periodic waves, offering deep insights into wave dynamics. Qualitative analysis is conducted through sensitivity analysis, bifurcation analysis, and chaos analysis. Sensitivity analysis examines the system’s behavior under varying initial conditions and parameter changes, while bifurcation analysis explores structural shifts in dynamics. Additionally, chaos analysis provides a comprehensive understanding of chaotic and periodic behaviors. This analysis is vital for grasping system dynamics, the influence of minor perturbations, and transitions between stable and chaotic states. Our work is further compared with existing studies, demonstrating its novelty and uniqueness

This study presents a comprehensive analysis of infectious disease dynamics through mathematical modeling and optimal control strategies. The primary objective is to derive insights into disease transmission by employing a model structured on integer and Caputo fractional order derivatives (CFOD). Initially, we establish the feasible region and confirm the boundedness of the model. Subsequently, the disease-free equilibrium (DFE) points and the basic reproduction number ($\mathcal {R}_{0}$) are analytically determined. Using fixed point theory, we rigorously prove theoretical results relevant to the model. To approximate solutions, we apply the Modified Euler’s Method (MEM), which demonstrates the model’s capacity to simulate disease dynamics with increased realism. Finally, optimal control analysis reveals that an integrated application of all four control strategies significantly reduces the infected population, thus enhancing the recovery rate.

We investigated the flow characteristics of Casson fluid over a vertical stretching sheet, considering a steady, incompressible, and laminar boundary layer. The rheological behavior of the non-Newtonian fluid is modeled using the Casson fluid framework, while the energy equation accounts for thermal radiation via the Rosseland approximation and incorporates variable thermal conductivity. A uniform magnetic field is applied perpendicularly to examine its influence on flow behavior, along with the Soret and Dufour effects to capture the coupled impact of thermal and concentration gradients. The governing nonlinear partial differential equations are transformed into nondimensional forms using appropriate transformations. A generalized model is developed using the Caputo fractional derivative via the Taylor series approach, and the fractional-order system is solved using the finite difference method. Results highlight the significant influence of key parameters, including the Casson parameter, thermal radiation, variable thermal conductivity, and Soret and Dufour numbers, on velocity, temperature, and concentration profiles. The findings demonstrate that increasing the Casson parameter enhances velocity, while thermal radiation and variable thermal conductivity strongly affect heat transfer rates. The study provides insights into optimizing flow control and heat transfer in industrial and engineering applications.

The present study highlighted the capability of machine learning (ML) models, combined with the empirical Natural Resources Conservation Service (NRCS) method, to simulate the complex rainfall-runoff relationship. The novel hybrid approach presented a robust foundation for enhancing runoff prediction by integrating ML’s pattern recognition ability with the NRCS method’s empirical reliability. Four hybrid ML-NRCS models were evaluated: Random Forest (RF) -NRCS, K-nearest neighbors (KNN)-NRCS, Extreme Gradient Boosting (XGBoost)-NRCS, and Decision Trees (DT)-NRCS. The evaluation utilized 31 years of daily rainfall data from the Erbil meteorological station, along with corresponding runoff data calculated using the NRCS method. The XGBoost-NRCS model outperformed other models by maximizing the coefficient of determination (R²) and Nash–Sutcliffe Efficiency (NSE) and minimizing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The XGBoost-NRCS model improved model generalization, providing a significant edge over other ML models in identifying critical patterns and handling sparse hydrological data. Furthermore, the findings revealed that all monthly-based models deliver better results than daily based models, suggesting that data aggregation enhances prediction accuracy. As a validation step, the models were tested using the dataset of the Sulaymaniyah meteorological station. The XGBoost-NRCS model yielded excellent predictions of monthly runoff but less effective predictions of daily runoff due to the high variability in daily rainfall data across diverse regions. A key finding of this study is the hybrid XGBoost-NRCS model’s strong predictive capability and broader applicability across different datasets. This study introduces a novel and scalable methodology for improving runoff estimation, particularly in data-scarce environments.

Fish harvesting has a major role in nutritive food that is easily accessible for human nourishment. In this article, a reaction-diffusion fish harvesting model with the Allee effect is analyzed. The study of population models is a need of this hour because by using precautionary measures, mankind can handle the issue of food better. The basic mathematical properties are studied such as equilibrium analysis, stability, and consistency of this model. The Implicit finite difference and backward Euler methods are used for the computational results of the underlying model. The linear analysis of both schemes is derived and schemes are unconditionally stable. By using the Taylor series consistency of both schemes is proved. The positivity of the Implicit finite difference scheme is proved by using the induction technique. A test problem has been used for the numerical results. For the various values of the parameters, the simulations are drawn. The dynamical properties of continuous models, like positivity, are absent from the simulations produced by the backward Euler scheme. Implicit finite difference scheme preserves the dynamical properties of the model such as positivity, consistency, and stability. Simulations of the test problem prove the effectiveness of the Implicit finite difference scheme.

Examining the model of ocean system by observing how climate changing in the wold wide as a result of predators is the main goal of this study. We have created a mathematical model to study the rates of ocean system due to climate change impact after implementing different measures such as continuous effect of different types of variables. The Fractal-Fractional operator (FFO) is used to convert the model into a fractional-ordered model for continuous monitoring climate change, providing reliable numerical solutions. Qualitative and quantitative analysis are derived to ensure the stability of the newly constructed fractional-order ocean system. Analyzing the model’s boundedness and uniqueness provides accurate results and helps in our understanding of its complicated dynamics. An analysis was conducted to study how the ocean system impact due to climate change affects under various factors. The rate of climate change under ocean system in each sub-compartment is calculated using the norm, and then verified for accuracy using Lipschitz conditions. The Hyers-Ulam stability of the model is also studied to understand the overall impact of the climate changes in different stages. Flip bifurcation of the ocean system impact due to climate change is analyzed and also explained by simulation like as effect of each parameter on the ocean system variables in specified range. Additionally, the impact of the fractional operator on the generalized form of the Mittag-Leffler kernel is explored using a two-step Lagrange polynomial technique for reliable solution. The effects of different factors effecting the climate change on ocean system are illustrated using numerical simulations using MATLAB. Simulations has been made to see the real behaviour and impact of climate changes in different stages of ocean system under different parameters aspects. This research will aid in understanding climate changes impact and developing management strategies based on verified findings for the environment.

The Sekenke-Kirondatal area in Tanzania boasts a rich gold presence, as exemplified by the presence of artisanal mines. However, geophysical signatures related to gold mineralisation have not been well understood. This study integrates high-resolution magnetic, radiometric, gravity and VTEM datasets, along with field geological information, for the first time to investigate the subsurface structures related to gold mineralisation. The results reveal newly identified faults that were previously not identified trending in NW–SE, distributed across most parts of the area. Some of these faults indicate a continuation of previously recognised NW–SE faults, which were found to terminate due to sediment cover. The K/eU and K/eTh ratio images reveal alteration zones along the NW–SE mineralised structures. The low- conductive bodies associated with mineralisation correspond to intermediate and high-density gravity anomalies and rocks with high magnetic susceptibilities. Spectral depth analysis indicates that the depths to the magnetic sources are 159 m for deep-seated anomalies and 111.9 m for shallow-seated anomalies, while source parameter imaging in the mineralised area shows depths ranging from 100 to 185 m. The integrated structural density map, combined with existing artisanal mining pits and geological information, indicates a new potential prospect in the northeastern part of the area yet to be unveiled, which could benefit artisanal miners when fully explored. These findings are crucial for assisting stakeholders and policymakers to further explore gold mineralisation in known and newly identified prospect areas, prompting sustainable development in the mining sector through artisanal mining.