Search Results (634)

A three-year (January 2020–December 2022) daily dataset of 16 polycyclic aromatic hydrocarbons (PAHs) collected in parallel with PM_2.5 and a suite of meteorological variables at a coastal Mediterranean urban site in southern Italy (Pomigliano d’Arco, Campania) is presented and analysed. Raw PAH time series were decomposed into a long-term trend component (LT), a seasonal component (ST), and a residual component (RT) using an iterative missing-value-robust Kolmogorov–Zurbenko (KZ) moving-average filter. Spearman rank correlations between PAH concentrations and four meteorological predictors (mean temperature, relative humidity, mean wind speed, and maximum wind speed) were computed for each congener. Diagnostic molecular ratios—Fla/(Fla + Pyr), BaP/BghiP, Indeno[1,2,3-cd]pyrene/(IcdP + BghiP), and BaA/(BaA + Chr)—were evaluated seasonally and interpreted jointly with an information-theoretic Bayesian mixture modelling procedure (SNOB/MML) and with the documented susceptibility of some PAH ratios, especially BaP-containing ratios, to atmospheric ageing, phase repartitioning and summer photodegradation. Total PAH concentrations (sum of 16 congeners) ranged from <1 ng m⁻³ in summer to 46 ng m⁻³ during winter high-pollution episodes, with BaP peaking at ≈6.7 ng m⁻³. Because BaP was measured in the PM_2.5 fraction, comparisons with the EU annual target value of 1 ng m⁻³ established for PM₁₀-bound BaP are treated as indicative context only, not as formal compliance statements. Pronounced seasonal variability was driven primarily by residential heating emissions, and the incremental lifetime cancer risk (ILCR) for inhalation exposure reached $1.03\times {10}^{-4}$ (95% CI: $0.88$–$1.20\times {10}^{-4}$) during the heating season under a continuous outdoor-exposure worst-case scenario. The absolute ILCR magnitude is conditional on the selected TEF scheme and on the adopted BaP unit-risk coefficient; under an additional indoor-dominated scenario (16 h day⁻¹, infiltration factor 0.6), the corresponding risk remained above the conventional ${10}^{-6}$ benchmark. An anomalous near-background PAH signal during spring 2020 is attributed to the COVID-19 national lockdown, which reduced total PAH concentrations by approximately 85% relative to the seasonal component predicted by the iterative moving-average filter for the same calendar window. Source apportionment via diagnostic ratios identifies residential/biomass combustion as the dominant cold-season source and vehicular emissions as the prevailing warm-season source. These results provide a novel characterisation of PAH pollution dynamics in the undersampled southern Mediterranean and provide evidence to support targeted abatement policies. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article

This study investigates the integration of categorical inverter alarm data into data-driven frameworks for photovoltaic (PV) system monitoring. While most existing approaches rely exclusively on continuous SCADA measurements, the potential of categorical operational data remains largely unexplored. In this work, categorical alarm signals are incorporated into power forecasting to enable anomaly detection. The proposed approach is evaluated on a large-scale real-world dataset comprising multiple PV plants and more than 100 inverters, representing over 1000 inverter-years of operation. The four most popular time series forecasting models, including Multi-Layer Perceptron, Long Short-Term Memory, Extreme Gradient Boosting, and Mamba, are used to estimate power output from continuous inputs, while categorical variables are integrated using one-hot encoding and entity embeddings. Anomaly detection is performed by analyzing residuals between predicted and measured power output. The results show that categorical alarm data contain relevant operational information and can be effectively incorporated into forecasting-based monitoring frameworks. However, their impact on predictive performance varies depending on the encoding strategy and model choice, highlighting important trade-offs between model complexity and feature representation. By providing a systematic evaluation of categorical data integration across a large, diverse dataset, this work addresses a gap in the literature and establishes a benchmark for future research on hybrid continuous–categorical approaches for PV inverter monitoring. Full article

Surface deformation is characterized by long-term accumulation and significant spatial differences, commonly inducing ground fissures and structural damage to roads and buildings, and in severe cases, causing collapses and other accidents that directly threaten human life. Reliable deformation monitoring and prediction are of great significance for early warning and infrastructure safety. Synthetic aperture radar interferometry (InSAR) technology can be used to acquire high-spatiotemporal-resolution temporal observations of surface deformation over a large area, but research on surface deformation prediction using InSAR temporal images is still relatively limited. Therefore, in this study, we used Sentinel-1 temporal imagery as the data foundation. Firstly, small baseline subset (SBAS)-InSAR inversion was used to obtain the line-of-sight-oriented cumulative deformation sequence and subsidence rate results. Based on this, the causal multi-trend fusion patch-to-point (CMTF-P2P) surface deformation prediction framework was developed. This framework effectively separates the long-term trends and short-term fluctuations in the deformation sequence through causal decomposition; introduces external drivers such as rainfall and event phase characteristics to enhance the temporal expression; and utilizes patch-to-point fusion of neighborhood spatial information to improve the spatial continuity and the ability to characterize local differences. Experimental results show that the method has an RMSE of 0.43 mm and an R² of 0.92, outperforming time-series deformation prediction models such as LSTM and iTransformer. Compared with traditional models that learn overall change patterns from historical time series, this paper alleviates the confusion between trends and volatility using causal STL decomposition, making the model’s expression of long-term trends and seasonal and short-term fluctuations in volatility clearer; event phase encoding enhances the model’s ability to characterize sudden disturbances and phased responses to events; the patch-to-point structure incorporates spatiotemporal information within the neighborhood, enhancing the model’s ability to apply spatiotemporal information; multi-branch collaborative modeling enhances the model’s ability to characterize multi-scale temporal features; and incremental cumulative consistency constraints enhance the physical consistency of the prediction results. Full article

As representatives of early-flowering herbaceous peony types, certain cultivars known as the ‘Coral’ series are highly prized in the global cut flowers market for their unique dynamic color transitions from orange-red (amber) to creamy yellow during the florescence and senescence periods. Despite their strong growth vigor and high commercial value, these cultivars face critical postharvest preservation challenges, most notably rapid petal abscission and short vase life. Previous studies have confirmed that postharvest quality deterioration of these peony cut flowers, including undesired color fading and accelerated senescence of petals, is closely associated with ethylene and ROS accumulation. To address these development impediments, systematic optimization of the entire industrial chain is essential. Proposed countermeasures include preharvest regulation of environmental conditions and cultivation practices to establish a foundation for quality formation, as well as postharvest strategies such as precise harvest timing, anti-ethylene treatments, and full cold-chain logistics. Meanwhile, simplifying the distribution system and optimizing terminal vase preservation techniques are also crucial to maintain postharvest quality. In the long term, promoting sustainable development of the global cut-flower industry will require breeding new germplasm with low ethylene sensitivity from a global perspective, continuously optimizing agronomic practices to overcome year-round supply constraints, and accelerating the application of intelligent technologies such as the Internet of Things (IoT) in full chain quality management. Full article

Predictive Maintenance (PdM) approach in Combined Sewer Systems (CSS) is gaining momentum due to advances in sensor technology, affordability and availability of data, and the rise of machine learning and data analytics. This study aims to characterize the general behavior of CSS under Dry and Wet weather conditions. To achieve this, 10 min resolution precipitation and water level data were collected from nearby SIAS stations and AMAP radar water level sensors, installed at the outlet chamber of the CSS, respectively. Precipitation data was used to segment continuous time series data into Dry Weather Flow (DWF) and Wet Weather Flow (WWF). DWF analysis exhibited unique flow patterns that strongly correlated with water consumption behaviors of households. For wet weather, a comparison was made between key rainfall parameters (depth, intensity) and peak water level data, and nonlinear relationships were observed that highlight the complex rainfall–runoff process. These findings underscore the need for separate predictive models tailored to DWF and WWF characteristics. Integrating high-resolution sensor data with machine learning models such as Long Short-Term Memory (LSTM) networks and anomaly detection, Autoencoders can enhance PdM, improving CSS management and reducing risks of blockage events and infrastructure failures. Full article

Large trees are keystone ecological structures that sustain biodiversity and ecosystem services, particularly in human-altered landscapes. However, their persistence is increasingly threatened by land-use change, urban expansion, and inadequate monitoring. This study develops and validates a scalable, automated framework for monitoring the loss of large individual trees using satellite image time series and breakpoint detection. We compared four spectral indices (SIs): Enhanced Vegetation Index 2–EVI2; Normalized Burn Ratio–NBR; Normalized Difference Red Edge–NDRE, and the Normalized Difference Vegetation Index–NDVI derived from Sentinel-2 imagery (2015–2025) for 691 georeferenced trees in Lousada, northern Portugal. Data were accessed and processed in Google Earth Engine and analyzed using a custom R-based workflow, including cloud masking, gap-filling, temporal interpolation, upper-envelope smoothing, deseasonalization, and break detection. Five breakpoint detection algorithms were compared: BFAST, energy-divisive, linear regression of structural changes, wild-binary segmentation, and change point models. Detected breakpoints were subsequently post-validated to determine whether they were associated with declines in SIs, using three pre-/post-breakpoint methods: comparisons of short- and long-term medians and a randomized trend analysis. As a baseline, these algorithms/post-validation logic were compared against the Continuous Change Detection and Classification (CCDC) approach. The results indicate moderate but consistent break detection performance, with a maximum balanced accuracy of 73% (for EVI2 or NDVI and using the energy-divisive algorithm coupled with the long-term median post-validator) under conservative validation criteria and high specificity for surviving trees. CCDC ranked comparatively lower at 62%. Algorithm performance varied substantially, with the energy-divisive providing the most conservative detection and the wild-binary segmentation yielding higher sensitivity. Performance was further influenced by tree structural attributes and species identity, with larger, taller and isolated trees, as well as particular genera, showing higher detection accuracy, with genus Eucalyptus, Tilia and Celtis yielding top performance results (79–65%) and Quercus, Castanea and Platanus the lowest (62–60%). By integrating satellite observations with large-tree inventory data from the Green Giants citizen science project, this study demonstrates the potential of decentralized, Earth observation-based monitoring to support tree-level loss assessments in fragmented landscapes. The proposed framework provides a transferable foundation for wide-scale monitoring of large trees in peri-urban and mixed-use environments. Full article

Coal-mine-gas-monitoring data exhibits missing phenomena due to the harsh underground operating environment. Accurate imputation of missing values in gas-monitoring sequences serves as a key data foundation for guaranteeing the continuity of gas data, enhancing the reliability of disaster early warning, and improving the accuracy of mine safety situation analysis and judgment. Aiming at the prevalent random and segmented missing issues in coal-mine-gas-monitoring time-series data, and the limitation that existing imputation methods struggle to accurately capture the nonlinear spatiotemporal correlations and long-range temporal dependencies of such data, this study proposes a missing data imputation method for coal mine gas time-series data based on the Spatio-Temporal Graph Attention Network—Echo State Network (ST-GAT-ESN). Firstly, this method extracts temporal features of the gas concentration sequence using a Gated Recurrent Unit (GRU). Subsequently, it models multiple monitoring points as graph nodes through a Graph Attention Network (GAT), constructs an adjacency matrix based on airflow propagation relationships, and adaptively learns the spatial dependency weights between monitoring points to realize the deep fusion of spatiotemporal features. Finally, it designs a dual-channel Echo State Network (ESN), synchronously inputs the spatiotemporal fusion features of the missing regions before and after, efficiently fits the nonlinear evolutionary trend of the data by virtue of the echo state property of the reservoir, and solves the output layer weights through ridge regression to achieve accurate imputation of missing values. Experimental results demonstrate that, compared with the single-ST-GAT-ESN, ESN, and ARIMA models, the proposed method achieves the optimal imputation performance in both random and segmented missing scenarios within the missing rate range of 5–50%. The three evaluation metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—are reduced by 30–80% compared with the benchmark models. Moreover, the imputation curve achieves the best fitting performance with the ground-truth curve at a 50% segmented missing rate. This study confirms that the ST-GAT-ESN model effectively enhances the adaptability and robustness to complex missing patterns via spatiotemporal collaborative modeling and a dual-channel fusion mechanism, providing a high-precision and highly stable technical solution for ensuring the integrity of coal-mine-gas-monitoring data, and also provides theoretical references and engineering insights for the missing-value processing of industrial time-series monitoring data. Full article

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

Quantitative land cover records for geopolitically restricted regions remain extremely scarce, particularly for the pre-Landsat era. This study reconstructs long-term urban land cover dynamics in Pyongyang, Democratic People’s Republic of Korea (DPRK), over a 55-year span (1967–2022) by combining deep learning colorization of declassified CORONA KH-4 panchromatic imagery with the GLC_FCS30D global 30 m land cover dynamics dataset. The GLC_FCS30D nine-epoch time series (1985–2022) revealed that built-up area expanded from 65.0 km² (36.0%) to a peak of 103.2 km² (57.1%) in 2015, driven almost entirely by the conversion of agricultural land, before declining to 92.7 km² (51.3%) by 2022. The 1967 colorization-based classification yielded a built-up proportion of 35.9%, closely approximating the 1985 baseline. Integration of these results identified three urbanization phases: post-reconstruction consolidation (1967–1985), sustained expansion at the expense of agricultural land (1985–2015), and stabilization coinciding with intensified international sanctions and pandemic-related isolation (2015–2022). The near-halving of agricultural land within the capital’s vicinity during chronic national food insecurity is consistent with a fundamental tension between showcase urban modernization and food production imperatives in state-planned economies. As perhaps the last continuously state-planned socialist city, Pyongyang’s trajectory offers a rare empirical counterpoint to market-driven urbanization processes. Full article

Treatment intensification with androgen receptor pathway inhibitors (ARPIs) and/or docetaxel in addition to androgen deprivation therapy (ADT) improves survival for men with metastatic hormone-sensitive prostate cancer (mHSPC), yet real-world uptake has historically been low. We conducted a population-based retrospective cohort study of Ontario men aged ≥66 years diagnosed with de novo mHSPC between 2014 and 2022 using linked administrative health data, defining treatment intensification as initiation of an ARPI and/or docetaxel with ADT within six months of diagnosis. Quarterly intensification rates were modeled using autoregressive integrated moving average (ARIMA) time-series methods with nonlinear trend specifications, and competing models were compared using information criteria, out-of-sample hold-out forecast accuracy, and long-horizon extrapolation behaviour to project uptake through 2030. Among 6099 men, 24% received treatment intensification, with quarterly intensification rates increasing from 3% in 2014 to 56% in 2022. A restricted cubic spline ARIMA model (ARIMA(1,0,1) + RCS3) was selected as the primary base-case forecast because it showed superior out-of-sample hold-out accuracy and more tempered long-horizon extrapolation. The cubic specification was retained as an upper-bound scenario, reflecting the possibility of continued aggressive momentum in treatment adoption. Both specifications captured a marked inflection after 2020 that temporally coincided with guideline updates and funding expansions. Near-term base-case projections (through 2026) suggest continued growth in intensification toward 80–85%, with the upper-bound scenario approaching saturation more quickly. Projections beyond 2026 are exploratory and presented for methodological completeness, given the eight-year horizon relative to a nine-year observation window and the widening prediction intervals at extended horizons. Despite substantial growth over time, treatment intensification remains incomplete in routine practice. These findings are temporally consistent with the impact of policy and funding changes on the adoption of evidence-based therapy and underscore the need for ongoing implementation efforts to address persistent clinical and system-level barriers to equitable access. Full article

Background: Hospital-acquired multidrug-resistant organism (HA-MDRO) infections remain a major patient-safety threat linked to antimicrobial exposure, but long-term hospital-level evidence on whether integrated stewardship can reduce HA-MDRO burden remains limited. Methods: We conducted a quasi-experimental interrupted time-series study at a large multi-campus tertiary teaching hospital in China. A hospital-wide integrated intervention combining diagnostic stewardship and antimicrobial prescribing stewardship was implemented on 1 November 2021. Monthly aggregated hospital data from July 2018 to December 2024, including 2,145,489 hospitalizations, were analyzed. The primary outcome was HA-MDRO infection incidence density per 1000 patient-days. Results: HA-MDRO incidence density decreased immediately at the start of the COVID period (IRR = 0.246; p < 0.001) and then increased over time (IRR per month = 1.074; p < 0.001). After intervention implementation, the post-intervention trend declined significantly relative to the COVID-period trajectory (IRR per month = 0.938; p < 0.001). Microbiological testing increased immediately and continued to rise (OR = 1.381 and 1.016 per month, respectively), whereas restricted antibiotic use declined after implementation (OR = 0.979 per month; all p < 0.05). The control outcome showed no consistent post-intervention change. Counterfactual analysis estimated that 15,274 HA-MDRO cases were averted over follow-up. Conclusions: A hospital-wide integrated stewardship intervention was associated with reversal of the increasing HA-MDRO trajectory observed during the COVID period, together with improved microbiological testing and reduced restricted antibiotic use. These findings support the value of integrating diagnostic and prescribing stewardship in high-volume tertiary hospital settings. Full article

Neural ordinary differential equations (Neural ODEs) describe the feature evolution of deep networks by continuous-time dynamical systems and enable end-to-end learning through differentiable numerical solvers. Nevertheless, in closed-loop rolling prediction for small-sample time series, conventional Neural ODEs remain vulnerable to error accumulation and numerical instability. To improve the controllability of long-term evolution, this study proposes a neural ordinary differential equation framework based on fractional-order operators. Rather than directly introducing full-history convolution kernels into the governing dynamics, the proposed approach constructs a fractional effective step size from the closed-form expression of the Riemann–Liouville fractional integral of a constant function and consistently embeds it into all sub-steps of a fourth-order Runge–Kutta solver. In this way, the scale of continuous-depth propagation is regulated by a single tunable parameter. Combined with a residual output structure, the method preserves the interpretability of continuous dynamics while effectively suppressing trajectory drift in closed-loop prediction and improving training stability. To investigate the impact of the fractional-order parameter on fitting and extrapolation, particle swarm optimization is employed to search automatically for the optimal order. Experimental evaluations on the linear spiral system and Lorenz continuous dynamical systems and on a small-sample provincial annual electricity-consumption dataset show that the proposed model achieves lower prediction errors across multiple tasks and exhibits superior trajectory preservation and robustness under long-horizon forecasting. Full article

With the in-depth advancement of the “Digital China” initiative, policies and research discourses related to the digital economy have continued evolved, making it necessary to systematically examine their stage-specific characteristics and underlying logic from a long-term perspective. Accordingly, this study adopts information society theory as the analytical framework and selects the annual series of reports on China’s digital economy development published by the China Academy of Information and Communications Technology (CAICT) from 2015 to 2024 as the research corpus. Using text mining techniques and Latent Dirichlet Allocation (LDA) topic modeling, this paper conducts a longitudinal examination of the stage-wise systemic evolution of key topics in China’s digital economy development. The findings indicate that over the past decade, the topic structure of China’s digital economy has followed a clear evolutionary trajectory, progressing from “informatization-driven development” to “platform expansion,” and subsequently to “data factors and institutional governance.” In the early stage, the focus was on information infrastructure development and industrial integration; the middle stage shifted toward the platform economy and enterprise growth; more recently, the emphasis has increasingly been placed on the construction of data factor markets and the improvement of governance frameworks. This process of topic evolution not only reflects changes in the practical forms of the digital economy but also reveals the ongoing adjustment of the state’s cognitive framework and governance logic regarding digital economy development. These findings provide empirical evidence for understanding the systemic evolution of China’s digital economy over time. By identifying the stage-specific pathways of China’s digital economy, this study extends the application of information society theory within this context and provides new empirical evidence for understanding the evolutionary logic underlying high-quality digital economy development. Full article

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning. Full article