Search Results (962)

Remote sensing research increasingly relies on heterogeneous satellite, UAV, hyperspectral, multispectral, SAR, and environmental monitoring data to support land, urban, hydrological, and environmental applications. However, these data are often affected by sensor differences, spatial and temporal heterogeneity, missing observations, irregular sampling, noise, and non-stationary environmental processes. This systematic review was conducted within the context of the Romanian Hub for Artificial Intelligence (HRIA) project, which supports the development of strategic artificial intelligence technologies. The review synthesizes current research on adaptive neural networks for remote sensing and Earth observation, with particular attention to Liquid Neural Networks and related continuous-time neural models. A systematic search was conducted across IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, Wiley Online Library, Google Scholar, and reference lists. After duplicate removal, screening, and full-text assessment, 61 studies published between 2018 and 2026 were included in the qualitative synthesis. The findings show that adaptive neural networks have gained increasing attention after 2022 and are mainly applied to image-centered remote sensing tasks, including classification, mapping, object detection, segmentation, enhancement, and change detection. Most studies adapt established deep learning architectures through multi-scale processing, adaptive feature fusion, attention mechanisms, graph relationships, or task-specific refinement. Continuous-time models are used less frequently but are relevant for irregular observations and dynamic environmental processes. Liquid Neural Networks remain emerging, and current evidence suggests only preliminary, task-specific relevance for irregular, noisy, multimodal, and dynamic remote sensing applications. Full article

Air pollution poses a major environmental and public health challenge in Metropolitan Lima, Peru, where complex topography, coastal meteorological conditions, and intense urbanization generate highly dynamic patterns of pollutant concentrations. Accurate multi-horizon forecasting is therefore essential for supporting environmental monitoring and early-warning systems. This study proposes a hybrid deep learning framework, called VMD-Attention-BiLSTM, for forecasting hourly PM_2.5, PM₁₀, and NO₂ concentrations using a decade of hourly air quality observations (2015–2024) collected by the National Meteorology and Hydrology Service of Peru (SENAMHI). The proposed methodology integrates a strictly causal preprocessing pipeline—including forward-only imputation, spatial-corroborated percentile-95 outlier detection, and pollutant-calibrated Variational Mode Decomposition (VMD)—with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a Bahdanau-style attention mechanism. All transformations are fitted exclusively on the training partitions of a five-fold TimeSeriesSplit cross-validation to prevent information leakage. A systematic benchmark of 1008 imputation experiments was conducted to justify the choice of causal linear interpolation over Kalman Filter alternatives. Model performance was evaluated at 24-, 48-, and 72-h forecasting horizons. The optimized framework achieved competitive predictive performance across the evaluated horizons, yielding best RMSE values of 9.04, 19.93, and 9.41 µg/m³ for PM_2.5, PM₁₀, and NO₂ at 24 h, degrading to 9.79, 24.56, and 11.02 µg/m³ at 72 h. All metrics are reported on the original concentration scale. VMD sensitivity analysis revealed that the optimal mode count is pollutant-dependent ($K=12$ for particulate matter; $K=4$ for NO₂). Furthermore, the ablation study showed that the complete VMD-Attention-BiLSTM configuration provided competitive and frequently improved performance relative to the baseline and partial configurations, with the magnitude of the improvement varying according to pollutant and forecasting horizon. The obtained results indicate that integrating signal decomposition with attention-based bidirectional learning can provide complementary benefits for forecasting under highly non-stationary urban conditions, particularly at shorter forecasting horizons. The proposed framework provides a reproducible and scalable solution for intelligent air-quality forecasting and serves as a valuable decision-support tool for environmental monitoring and public health protection in complex metropolitan environments. Full article

Biotope mapping provides an essential spatial framework for biodiversity conservation, ecosystem management, and sustainable landscape planning. However, a standardized and transferable GIS-based methodological framework for biotope identification and mapping is still lacking in Türkiye, limiting the systematic integration of biodiversity considerations into spatial planning processes. This study develops an integrated GIS-based biotope mapping framework for Çınarcık District, Yalova Province, Türkiye, integrating Digital Elevation Model (DEM), CORINE Land Cover 2018, Forest Management Plans, stand characteristics, vegetation, floristic, and hydrological data through spatial analyses. Additionally, 30 national and 15 international studies were systematically reviewed to identify the common indicators, data sources, and methodological components used in biotope mapping and to establish the proposed GIS-based framework. The proposed approach identified four main biotope groups (forest, aquatic, agricultural, and urban). Based on ecological similarity and growing environment characteristics, 12 sub-biotope types were identified within the forest biotopes. Forest biotopes were the dominant ecological units, mainly characterized by broadleaved communities dominated by Fagus orientalis, Castanea sativa, Tilia tomentosa, and Quercus petraea. A total of 72 forest stand types were identified, with Fagus orientalis-dominated forests in plateau environments representing the largest sub-biotope type (36.40%). The proposed framework provides a repeatable and transferable, inventory-based methodology that can serve as a preliminary decision-support tool for biodiversity assessment, conservation planning, and sustainable landscape management in forested landscapes with similar ecological characteristics, pending future field-based validation. Full article

Nature-Based Solutions (NBSs) are increasingly recognized as strategic tools for climate adaptation and urban regeneration, yet their implementation requires balancing environmental performance, territorial identity, and governance feasibility. This article investigates the application of the euPOLIS H2020 methodology in Palermo, Italy, focusing on the proposed transformation of Villa Turrisi—a residual peri-urban agricultural area representing one of the last remnants of the historical Conca d’Oro landscape—into a new public green infrastructure within the framework of the new Municipal Development Plan (PRG/PUG). This study examines how a structured co-design process involving municipal actors, citizens, and local associations can be coupled with ecosystem service assessments. Rather than selecting a single optimal design, the analysis evaluates two alternative vision scenarios—a multifunctional framework aligned with euPOLIS principles and an intensive urban forest model—to generate quantitative and qualitative baseline data. Environmental indicators (carbon sequestration, microclimatic regulation, and hydrological resilience) are used not as deterministic selection criteria but as theoretical decision-support evidence within a predominantly qualitative, value-driven local planning process. The core contribution of this work lies in demonstrating the operational transferability of the euPOLIS framework from front-runner to follower cities within a Mediterranean planning context. The Palermo case shows how quantitative environmental simulations can effectively inform—rather than dictate—participatory governance, heritage preservation, and regulatory feasibility. Ultimately, this article offers a transferable planning and policy framework that bridges European NBS research with municipal decision-making, providing actionable insights for integrating climate-resilient green infrastructure into local urban plans. Full article

This study develops an interpretable and validated XGBoost–SHAP framework integrating multisource geospatial data and historical flood observations, with model performance evaluated using independent validation and future projections driven by bias-corrected CMIP6 climate scenarios to characterize the spatiotemporal variations and contributions of flood driving factors across three regions of Kazakhstan (2000–2025). The results demonstrate pronounced spatial differences in flood-driving factors: delayed snowmelt coupled with orographic rainfall dominates flood variability in the mountainous Almaty Region; hydrological memory effects regulate flood responses in the Akmola plains; and socioeconomic exposure shows an increasing contribution to flood risk evolution in Turkestan. Future multi-scenario simulations indicate that the flood-affected area in the Almaty Region is projected to increase by 10.8% under SSP2-4.5, which is associated with enhanced snowmelt processes, whereas the Akmola Region may experience a 23.2% reduction under SSP5-8.5, which is associated with changes in evaporation–soil moisture interactions. The interaction between socioeconomic development and natural hazards results in divergent risk trajectories: urban expansion in Akmola and Turkestan may offset declining hydroclimatic hazards, creating a potential risk paradox, whereas mountainous regions remain sensitive to concurrent increases in hazard intensity and exposure. These findings indicate that flood risk evolution in the studied regions of arid Central Asia is being increasingly influenced by socioeconomic dynamics in addition to natural hazards, highlighting the importance of differentiated adaptive planning strategies. Full article

Artificial intelligence (AI) is increasingly used in water research. However, many AI models, particularly complex machine learning models, often generate outcomes that are difficult for humans to interpret. Explainable artificial intelligence (XAI) has been developed to address these challenges by providing transparent and human-interpretable explanations of model behavior and predictions. We conducted a structured narrative review using predefined searches of Web of Science Core Collection and Scopus to synthesize empirical XAI applications across six water-research domains: hydrological processes, water quality and pollution, groundwater systems, urban water systems, climate–water interactions, and water and wastewater treatment. The review covers feature-importance methods, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), partial dependence plots (PDPs), individual conditional expectation (ICE) plots, accumulated local effects (ALE) plots, counterfactual explanations, and deep-learning attribution methods. Building on previous reviews and perspectives focused on particular water domains or methodological priorities, we provide a cross-domain synthesis of XAI spanning natural and engineered water systems, with emphasis on method selection, model and data compatibility, explanation reliability, and operational implementation. These capabilities, however, must be interpreted with appropriate caution because XAI explanations remain conditional on the data, fitted model, and explanation method, and therefore should not be treated as evidence of causal mechanisms or environmental controls. Recognizing these limitations, we provide practical guidance for selecting and evaluating XAI methods and outline priorities for developing reliable, scalable, and operationally useful AI systems for water research and management. Full article

This study investigates how dust and sulfate aerosols modulate cloud properties and rainfall over Sudan, a key part of the Sahara–Sahel dust belt. Satellite and reanalysis products (MODIS, CHIRPS, MERRA 2, EAC4) are combined with four CMIP6 models to analyze rainy season (JJAS) aerosol optical depth (AOD), cloud water path (CWP), cloud effective radius (R_eff), and precipitation for 2003–2014, and to assess future changes under SSP1 2.6, SSP2 4.5, and SSP5 8.5 during 2041–2100. Reanalysis data show that natural mineral dust dominates aerosol loading over Sudan, accounting for approximately 70–85% of total annual mean AOD, with substantial spatial variability across the domain and the highest contributions occurring over the Sahara–Sahel transition zone, whereas sulfate AOD peaks over urban and agricultural regions in central and eastern Sudan. Observations reveal that dust AOD is negatively correlated with CWP and precipitation in northern and central Sudan, while sulfate AOD shows positive correlations with CWP and rainfall in the southeast. All datasets exhibit negative AOD–R_eff relationships that are consistent with a Twomey-like signature. However, because AOD is a column-integrated measure that does not directly represent cloud-based cloud condensation nuclei (CCN), these relationships should not be interpreted as direct evidence of the Twomey effect. The models also overestimate the positive AOD–CWP and AOD–precipitation correlations, suggesting that they may simulate stronger aerosol-related cloud persistence and precipitation responses than indicated by the observations. Multi-model projections indicate substantial twenty first century declines in sulfate and total AOD under all SSPs, driven by emission controls, whereas dust AOD shows weaker, climate- and land-use-controlled changes. Together, these results suggest that CMIP6 likely overestimates the sensitivity of Sudan’s hydrological cycle to aerosol perturbations and highlight the need for improved dust parameterizations and high-resolution regional modeling to constrain future water resource risks. Full article

Liquid Time-Constant networks (LNNs), recurrent models with adaptive, input-dependent time constants, were evaluated for water level prediction in an urban drainage system. The architecture was assessed on a ten-year measurement dataset from the Bellinge catchment and benchmarked against gated recurrent unit (GRU), long short-term memory (LSTM), temporal convolutional network (TCN), and multilayer perceptron (MLP) baselines. The best-performing LNN variant achieved the highest mean predictive accuracy in the benchmark (Nash–Sutcliffe efficiency, NSE = 0.849), with particularly accurate representation of the continuous response of the gravity-driven part of the network without substantial degradation during flash-flood events. The analysis showed that the adaptive time constants help distinguish system-wide hydraulic processes from local control actions while providing interpretable diagnostics of the model’s internal response scale. The principal challenge for the LNN was the intermittent operation of the pumping station; isolating the pump pathway in the dual-branch DB-LNN variant mitigated this performance degradation while preserving predictive accuracy at the remaining sensors. The ablation analysis indicated that architectural separation of the processing pathways reduced interference between the continuous hydraulic dynamics and the local switching dynamics of the threshold-controlled facility. Full article

Against the background of global climate change and rapid urbanization, the frequency of extreme hydrological events has increased markedly, and hydrological and geological hazards induced by extreme heavy rainfall have occurred frequently. Considering the joint occurrence characteristics of heavy rainfall hazards, statistical methods are used in this study to develop a mathematical model for calculating hazard–risk combinations and disaster-triggering thresholds that integrates copula functions and the Kendall return period. First, a bivariate joint-distribution probability model is established using copula functions. Then, the Kendall return period under the same recurrence level is calculated to infer combined hazard thresholds for multiple disaster-causing factors. Taking rainfall-induced flash floods and landslides as examples, combined hazard thresholds are calculated. Rainfall and discharge measured at the Heyuan hydrological station in the period from 2001 to 2020 are taken as a case study. The results show that (1) the optimal bivariate copula function for annual maximum 1-day rainfall and annual maximum discharge is the Clayton Copula; (2) similarly, the optimal bivariate combined probability function for rainfall-induced landslides is the Gumbel Copula; and (3) the combined hazard thresholds of multiple disaster-causing factors for rainfall-induced flash floods and landslides differ substantially from the thresholds based on single disaster-causing factors. Taking the return period of 100 years as an example, the univariate thresholds of discharge and rainfall are 8530.38 m³/s and 217.82 mm, while the combined thresholds decrease to 5793.10 m³/s and 175.22 mm. Meanwhile, the univariate thresholds of rainfall and landslide probability are 195.02 mm and 0.82, but the combined thresholds decrease to 170.81 mm and 0.78, respectively. In other words, conventional single-factor threshold calculation methods can severely underestimate the actual hazard level, and coupling among multiple factors can induce a significant disaster superposition and amplification effect. This study reveals the risk-coupling mechanism of heavy rainfall hazards and provides technical support for the accurate identification, prediction, and early warning of rainfall-induced flash floods and landslides under climate change. Full article

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts. Full article

Beaver dams can substantially modify flow conditions and increase local water retention, particularly in small urban and peri-urban streams exposed to hydrological alterations and increasing water deficits. This study evaluates the influence of beaver dams on hydraulic conditions, retention capacity, and water resources in the Junikowski Stream in Poznań, Poland. Field surveys, geodetic measurements, and spatial data were used to develop a one-dimensional hydraulic model in HEC-RAS. Three management scenarios were analysed: a channel without impoundment structures, the 2022 configuration including beaver dams and two artificial weirs, and the 2025 configuration representing a more developed beaver-dam cascade together with the functioning weirs. Simulations were conducted for a range of characteristic and probability flows to assess changes in water levels, inundation extent, and retained water volume. The results show that beaver dams exert the strongest effect under low-flow conditions, when they significantly increase water levels and improve local retention. Their hydraulic influence decreases with increasing discharge, although they continue to affect the spatial distribution of water in the valley. The proposed artificial structure may partly maintain retention benefits in the event of beaver dam degradation or removal. The findings demonstrate that beaver dams may function as effective nature-based solutions supporting water retention and potentially contributing to drought resilience and sustainable management of urban stream valleys. Full article

Contemporary approaches to urban water management increasingly rely on concepts such as Nature-Based Solutions (NBSs), Green Infrastructure, and Blue-Green Infrastructure. Although these frameworks have gained broad acceptance, their typological character provides limited guidance for project-oriented decision-making, as they primarily describe infrastructure types rather than their functions within integrated hydrological systems. To address this gap, this study proposes a complementary functional-operational framework for interpreting ecological infrastructures in urban flood mitigation. Employing a qualitative comparative case study methodology, we analysed four diverse international models—the Dutch Water Squares (Rotterdam), Tokyo’s underground flood control system, Copenhagen’s Cloudburst Management Plan, and Singapore’s ABC Waters Programme—to examine the systemic interaction between grey, green, and blue infrastructures at different watershed scales. The results indicate that flood mitigation effectiveness depends less on the predominance of a single infrastructure type and more on the functional coupling among them. Specifically, three primary functions were identified: rapid conveyance (grey infrastructure), infiltration and thermal regulation (green infrastructure), and dynamic storage and biodiversity support (blue infrastructure). Despite the contextual limitations and varying scales of the selected cases, blue infrastructure universally emerges as a systemic buffer that enhances urban resilience by regulating excess volumetric flows. Ultimately, the proposed framework introduces an actionable interpretative layer that complements existing typological classifications, providing planners and urban designers with a robust, scalable basis for implementing integrated ecological infrastructures. Full article

This paper examines the macro–micro impact of drought in South Africa using a computable general equilibrium model calibrated to the structure of the South African economy. Drought is represented as a severe supply-side shock: a 50% decline in total factor productivity in agriculture, forestry and fishing. The analysis traces how this shock is transmitted from agricultural production to prices, trade, employment, household income, consumption and welfare. The results show that agricultural output falls by 19.5%, agricultural prices rise by 43.3%, and agricultural imports increase by 84.8% as the economy shifts towards external supply. These sectoral effects generate wider macroeconomic losses, including a 1.0% decline in real GDP, a 1.7% increase in unemployment, a 1.2% fall in household income and a 1.5% reduction in household consumption. Welfare declines for both rural and urban households, but rural households experience larger losses because of their stronger dependence on agriculture, farm income, livestock assets and food markets. The findings show that drought is not only an agricultural or hydrological event; it is an economy-wide and distributional shock transmitted through production, price, trade and labour-market channels. Although imports help to cushion domestic scarcity, they do not fully offset higher prices or welfare losses. Policy responses should therefore combine drought-resilient agricultural investment, water-resource resilience, targeted social protection, food-supply stabilisation and rural livelihood diversification. Full article

While low-impact development and retention strategies are widely studied in urban and agricultural contexts, a distinct knowledge gap remains regarding the quantitative evaluation of dispersed, natural microretention structures in small, ungauged, mountainous forested catchments under complex topographic conditions. To address this limitation, this study provides a novel quantitative assessment of how natural bioretention interventions—specifically arcuate deadwood log barriers, cascading reservoir systems, and strategic afforestation—influence runoff reduction and substrate infiltration dynamics. Focusing on the 4.57 km² basin of the Stankowice Stream in southwestern Poland, the research integrates field geodetic and hydrological measurements with Iszkowski’s empirical flow formulas and high-resolution digital elevation modeling (SCALGO platform). Delineation of 10 key subcatchments revealed that surface runoff potential is heavily concentrated within specific flow pathways rather than determined solely by subbasin area. In unit No. 9, deploying an arcuate arrangement of 19 deadwood logs achieved an 11% reduction in surface runoff (retaining 8662.50 m³), whereas coupling these log structures with a downstream cascading two-dam system significantly enhanced retention performance by establishing 79,065.68 m³ of depression storage and driving 264,066.16 m³ of subsurface infiltration. Furthermore, multi-scenario land use modeling demonstrated that transforming land cover to forest reduced surface runoff by over 70% in topographically steep subcatchments (e.g., unit No. 7). These findings demonstrate that effective flood mitigation in headwater catchments requires a systemic, targeted hybrid strategy combining decentralized bioretention with localized storage nodes, offering a transferable framework for sustainable regional water governance. Full article

Accurate identification of land subsidence types is essential for effective urban risk management, yet remains challenging due to the superposition of deformation signals at different spatial scales and the complexity of urban environments. In this study, land subsidence types in Fuzhou were identified through an integrated framework combining multi-scale deformation analysis and ensemble learning. Ground deformation time-series measurements were derived from 66 Sentinel-1A synthetic aperture radar (SAR) observations acquired between January 2018 and June 2023 using the time-series interferometric synthetic aperture radar (TS-InSAR). Deformation values in decorrelated areas were subsequently reconstructed using regression models driven by multi-source geological, hydrological, land-use, and urban features, resulting in a spatially continuous deformation field. A Fast Fourier Transform (FFT)-based Butterworth filtering approach was then applied to separate regional-scale and local-scale subsidence signals. Based on the extracted local deformation patterns and discriminative auxiliary features, land subsidence was classified into five categories: farmland-related subsidence, linear infrastructure-related subsidence, low-lying stratum-related subsidence, land-use transition-related subsidence, and older building area-related subsidence. Three ensemble learning models, XGBoost, CatBoost, and LightGBM, were implemented for subsidence type classification. All models achieved satisfactory performance, among which LightGBM exhibited the best overall performance. The classification results reveal pronounced differences in spatial distribution and deformation intensity among subsidence types. Farmland-related subsidence occupies the largest proportion of the affected area but is characterized by relatively moderate deformation rates, whereas older building area-related subsidence, despite its limited spatial extent, exhibits the highest deformation intensity. This study demonstrates the potential of ensemble learning for land subsidence type classification. Full article