Climate change (CC) and land-use/land-cover (LU/LC) alterations are among the most pervasive drivers of hydrological and ecological change in river basins worldwide, yet their combined impacts on fluvial ecological diversity remain insufficiently quantified, particularly in data-scarce tropical regions. This study presents a novel integrated framework that jointly evaluates the individual and combined effects of projected CC and LU/LC change scenarios on flow regimes and aquatic ecological diversity in the Songkhram River Basin a critical tributary of the Mekong River in northeast Thailand. The Soil and Water Assessment Tool (SWAT) was calibrated and validated on a daily timescale for streamflow simulation. Bias-corrected and downscaled CMIP6 climate projections (2026–2055) under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) were combined with scenario-based LU/LC projections to simulate future flow regimes. Ecologically relevant flow alterations were assessed using hydrological alteration indices, and fluvial ecological diversity was quantified through the Shannon Diversity Index (SI). Results reveal that CC alone will significantly reduce annual streamflow (up to 22 This study develops an integrated modeling framework to assess the combined impacts of climate change (CC) and land-use/land-cover (LU/LC) change on river hydrology and ecological diversity. Using comprehensive datasets, including spatial information (land use, digital elevation model, soil, rivers, and population density), hydro-meteorological variables (rainfall, temperature), and future climate projections from multiple Global Climate Models under SSP2-4.5 and SSP5-8.5 scenarios, the research evaluates both natural and anthropogenic drivers of LU/LC change. Land-use dynamics were simulated using the Dyna-CLUE model, incorporating key driving factors such as topography, climate, and proximity to infrastructure, with logistic regression and policy-based scenario constraints. Hydrological processes were modeled using the SWAT model, calibrated and validated. The graphical abstract further depicts the impact assessment phase, where hydrological alterations are quantified and ecological responses are evaluated through the Shannon Diversity Index (SI). Scenario-based comparisons clearly demonstrate that both individual and combined effects of CC and LU/LC change influence streamflow regimes and ecological conditions. Overall, the graphical abstract succinctly depicts how integrated approaches that combine CC, SI, Dyna-CLUE, and SWAT provide a comprehensive and reliable tool for assessing ecological diversity under future changes, thereby supporting sustainable river basin management, ecological conservation, climate adaptation strategies, and informed policy decision-making. Framework to assess effect of CC and LU/LC change on river ecological diversity. Flow-based Shannon Index effectively reflected river ecological diversity. Under climate change, Shannon Index declines and ecological diversity is reduced. CC negative effects dominate over LU/LC positive impact on ecological diversity. Land-use conservation planning can serve as a potential adaptation to CC.
Soil heavy metals (HMs) pose a serious risk to the environment owing to their inherent accumulative and non-degradable characteristics. Artificial intelligence (AI) models demonstrated as powerful data-driven tools to mimic the existence of HM in soil and has so far contributed remarkably to the simulation of their environmental aspects. In this perspective, several concerns on the development of AI models for soil HM modeling were discussed. Data quality, optimal feature selections, models’ interpretability, transferability, and generalization have been debated to show how these limitations can cause to misleading or non-causal patterns, thereby directly impeding the rapid knowledge discovery phase from intelligent data-driven methods. As demonstrated in the graphical abstract, soil heavy metals (HMs) represent a critical environmental threat on earth system due to their non-degradable nature and tendency to accumulate over time. Artificial Intelligence (AI) models have emerged as a powerful tool for quantifying and simulating HM presence in soil, offering a sophisticated way to mimic complex environmental behaviors. However, there are several concerns in the literature required serious attention by developers including data quality, feature selection, and model interpretability. In addition, achieving better transferability and generalization is considered vital for reliable soil HM quantification across diverse geographic regions. In this communication article, those concerns were debated and discussed to provide better understanding of those concerns where an advancement proposed to facilitate the significant knowledge discovery, enabling more precise environmental risk assessments. By refining these models, researchers/developers/designers can better build a computer aid predictive models for HM distribution, ultimately supporting more effective remediation strategies to protect global food security and ecosystem health. Key challenges and unresolved issues in AI models for soil HM simulation were critically synthesized. Data quality, optimal feature selections, models’ interpretability were discussed for better AI models development. Model transferability and generalization were debated for better soil heavy metals quantification. We propose possible frontiers for unresolved challenges in soil heavy metals prediction under next-generation research era. We envisage that our critical contributions will lead to greater adoption of AI models for practical purposes in soil health applications.
This study examines the infection progression of tuberculosis (TB) and SARS-CoV-2 co-infection using a mathematical modeling framework combined with Artificial Neural Networks (ANNs). A system of ordinary differential equations is developed to characterize the interactions between the two diseases transmission. Numerical solutions generated through the 4^th -order Runge-Kutta (RK4) technique in MATLAB are employed to train the ANN model, with the dataset split into 81 The graphical abstract presents a comprehensive model for analyzing the co-infection dynamics of (TB) and SARS-Cov-2 through a mathematical model integrated with Artificial Neural Networks (ANNs). The main compartments are: susceptible 𝕊 , exposed classes 𝔼_1 and 𝔼_2 , infected classes 𝕀_1 and 𝕀_2 , co-infected class ℂ , and recovered population ℝ . A system of differential equations governs the transitions between these compartments. Numerical solutions are obtained using the RK4 approach in MATLAB, which are subsequently utilized to train the ANN model. The dataset was systematically partitioned into training (81
Accurate solar resource forecasting is essential for renewable energy expansion in climate-sensitive coastal regions, where atmospheric variability affects power generation and grid stability. In Bangladesh, the southeastern coastal zone experiences strong seasonal fluctuations driven by Bay of Bengal monsoon dynamics, creating challenges for reliable solar energy planning and operational forecasting. Despite advances in artificial intelligence, limited studies have integrated spatial climate interactions, long-term environmental observations, and explainable forecasting approaches for coastal solar resource assessment. This study develops an Earth System Intelligence framework for solar irradiance forecasting and energy potential mapping across the southeastern coastal region of Bangladesh. A harmonized 25-year dataset (2000–2025) was compiled from the Bangladesh Meteorological Department and NASA POWER, covering 12 coastal meteorological stations. Data were processed using the APS7C framework, incorporating nighttime masking, Butterworth filtering, normalization, and cyclical temporal encoding. Four forecasting architectures were evaluated: Spatio-Temporal Graph Attention Transformer (STGAT), Informer, CNN-LSTM-Attention, and a Hierarchical Ensemble Stack. The ensemble framework integrates Informer, CrossFormer, STGAT, and GRU base learners through a LightGBM meta-learner to capture spatial dependencies and temporal climate variability. The Hierarchical Ensemble Stack achieved the best predictive performance, with a correlation coefficient of 0.931 and a normalized standard deviation of 0.992. The Informer model maintained root mean square error values below 32 W/m² across 24–72 h forecasting horizons. The Hierarchical Ensemble Stack achieved the highest accuracy (correlation coefficient = 0.931; normalized standard deviation = 0.992), improving correlation by 8.4 This graphical abstract presents an Earth System Intelligence framework developed to support solar resource assessment and coastal energy planning in southeastern Bangladesh. The visual summary is organized into three interconnected components: Data Analysis, Models Learning Framework, and Results Applications. The first component highlights the integration of a 25-year coastal solar irradiance dataset (2000–2025) derived from the Bangladesh Meteorological Department (BMD) and NASA POWER across 12 meteorological stations. Data preparation was performed using the APS7C framework, which includes filtering, normalization, quality control, and feature engineering to enhance data reliability and model performance. The second component summarizes the forecasting architectures evaluated in the study, including Spatio-Temporal Graph Attention Networks (STGAT), Informer, CNN-LSTM-Attention, and a hierarchical ensemble framework with a LightGBM meta-learner. These models were designed to capture both temporal variability and spatial interactions within the coastal climate system. The final component presents the major outcomes and practical significance of the research. The ensemble framework achieved the highest predictive performance with a correlation coefficient of 0.931, while maintaining RMSE values below 32 W/m² across forecasting horizons. SHAP analysis identified the most influential predictors of solar irradiance variability, and spatial learning mechanisms revealed inter-station dependencies. The resulting framework provides a foundation for resource mapping, energy planning, grid integration, and data-driven renewable energy management in coastal environments. Developed an Earth system intelligence framework using 25 years of coastal climate observations. Integrated STGAT, Informer, CNN-LSTM-Attention, and ensemble learning for GHI forecasting. Hierarchical Ensemble Stack achieved R = 0.931 and normalized standard deviation of 0.992. SHAP analysis identified short-term irradiance persistence as the dominant predictor. Forecast outputs support energy planning, solar mapping, and smart-grid decision support.
Effective watershed management in semi-arid regions requires a precise understanding of how land use change (LUC) and climate change (CC) will jointly alter hydrological cycles. This study addresses a critical gap in assessing their relative impacts by evaluating the individual and combined effects of future LUC and CC on key hydrological variables in the Siminehrud River Basin, Iran, a region vulnerable to water scarcity. The Soil and Water Assessment Tool (SWAT) model was calibrated and validated for streamflow simulation. Future LUC for 2040 was projected using the Dyna-CLUE model under three distinct scenarios: sustainable development (MTSS), rapid expansion (MSSS), and business-as-usual (MBAU). Climate projections from five General Circulation Models under RCP4.5 were statistically downscaled using LARS-WG. Results indicated a marginal increase in annual precipitation (0.9 This graphical abstract provides a rapid, visually compelling summary of a comprehensive hydrological impact assessment study in the Siminehrud River Basin, Iran. It illustrates the principal methodological steps, beginning with the application of IPCC Representative Concentration Pathways (RCPs). Future climate projections from multiple Global Climate Models (under RCP4.5) and three distinct land-use scenarios for 2040, Sustainable Development (MTSS), Rapid Expansion (MSSS), and Business-as-Usual (MBAU), form the core inputs. These scenarios were developed using the Dyna-CLUE model, the results of which are visualized as classified land-use maps highlighting changes in Built-up areas, Irrigated cultivation, Rainfed agriculture, and Rangelands. The central flowchart depicts how these climate and land-use change projections are integrated into the SWAT (Soil and Water Assessment Tool) hydrological model for simulation. The right section presents a key comparative result, showing average monthly evapotranspiration (ET) and outflow (streamflow) variations for the combined scenarios against the baseline. The chart visually encapsulates the study's main finding: while future precipitation shows a slight increase, rising temperatures and land-use change drive a critical hydrological shift. The simulations project an overall decrease in annual streamflow alongside significant increases in ET under most scenarios. Crucially, the analysis revealed that land-use change was the dominant driver of hydrological alteration, surpassing the impact of climate change alone. This graphical abstract efficiently conveys the study's core warning: sustainable watershed management must explicitly address the trade-off between land-use development strategies and their unintended consequences on water scarcity, as visualized through the divergent pathways of streamflow and ET. Land-use change dominates future hydrological alteration, surpassing climate change impacts. Land-use intensification raises evapotranspiration (ET) while reducing streamflow, worsening scarcity. Future streamflow declines up to -3.1 to -4.5
