Search Results (395)

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

Wet areas, which are locations in the landscape that consistently retain moisture, and channel networks are important landscape compartments, with key hydrological and ecological functions. Hence, defining their spatial boundaries is an important step towards sustainable watershed management. In catchments of developing countries, wet areas and small order channels of river networks are rarely mapped, although they represent a crucial component of local livelihoods and ecosystems. In this study, topographic attributes generated with a 30 m SRTM DEM were used to map wet areas and stream networks of two tropical catchments in Central Brazil. The topographic attributes for wet areas were the local slope and the slope curvature, and the Topographic Wetness Index (TWI) was used to delineate the stream networks. Threshold values of the selected topographic attributes were calibrated in the Santa Maria catchment, comparing the synthetically generated wet areas and drainage networks with corresponding reference (map) features, and validated in the nearby Santa Maria basin. Drainage network and wet area delineation accuracies were estimated using random basin transects and multi-criteria and confusion matrix methods. The drainage network accuracies were 67.2% and 70.7%, and wet area accuracies were 72.7% and 73.8%, for the Santa Maria and Gama catchments, respectively, being equivalent or higher than previous studies. The mapping errors resulted from model incompleteness, DEM vertical inaccuracy, and cartographic misrepresentation of the reference topographic maps. The study’s novelty is the use of readily available information to map, with simplicity and robustness, wet areas and channel initiation in data-scarce, tropical environments. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

This study aims to enhance the accuracy and interpretability of flood susceptibility mapping (FSM) in Seoul, South Korea, by integrating automated machine learning (AutoML) with explainable artificial intelligence (XAI) techniques. Ten topographic and environmental conditioning factors were selected as model inputs. We first employed the Tree-based Pipeline Optimization Tool (TPOT), an evolutionary AutoML algorithm, to construct baseline ensemble models using Gradient Boosting (GB), Random Forest (RF), and XGBoost (XGB). These models were further fine-tuned using Bayesian optimization via Optuna. To interpret the model outcomes, SHAP (SHapley Additive exPlanations) was applied to analyze both the global and local contributions of each factor. The SHAP analysis revealed that lower elevation, slope, and stream distance, as well as higher stream density and built-up areas, were the most influential factors contributing to flood susceptibility. Moreover, interactions between these factors, such as built-up areas located on gentle slopes near streams, further intensified flood risk. The susceptibility maps were reclassified into five categories (very low to very high), and the GB model identified that approximately 15.047% of the study area falls under very-high-flood-risk zones. Among the models, the GB classifier achieved the highest performance, followed by XGB and RF. The proposed framework, which integrates TPOT, Optuna, and SHAP within an XAI pipeline, not only improves predictive capability but also offers transparent insights into feature behavior and model logic. These findings support more robust and interpretable flood risk assessments for effective disaster management in urban areas. Full article

Warm conditions during typically cold winters impact runoff and resulting hydraulic processes in channels where ice-cover would typically dominate. This field study on a short, low-slope reach in Southern Ontario, Canada, examined hydrologic and hydraulic processes with a focus on winter runoff events and subsequent bed shear stress variability. Through winter 2024, six cross-sections over a ~100 m reach were monitored near-weekly to measure hydraulic geometry and velocity profiles. These data characterized channel processes and estimated bed shear stress with law of the wall. In this channel, velocity increased more rapidly than width or depth with rising discharge and influenced bed shear stress distribution. Bed shear stress magnitudes were highest (means ranged ~2–6 N/m²) and most variable over gravel beds compared to the exposed bedrock (means ranged ~0.05–2 N/m²). Through a rain-on-snow (ROS) event in late January, bed shear stress estimates decreased dramatically over the rougher gravel bed, despite minimal changes in water depth and velocity. Pebble counts before, during, and after the event, showed that the proportion of finer-sized particles (i.e., <5 cm) increased while median grain size did not vary. These observations align with findings from both flume and field studies and suggest that milder winters reduce gravel-bed roughness through finer-sized sediment deposition, altering sediment transport dynamics and affecting gravel habitat suitability. Additionally, limited ice-cover leads to lower bed shear stresses and thus finer-sized materials are deposited, further impacting gravel habitat suitability. Results highlight the importance of winter hydrologic variability in shaping channel processes and inform potential stream responses under future climate scenarios. Full article

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article

In the present study, dam site suitability mapping was carried out for the Purna sub-basin of the upper Tapi basin. Constructing dams in strategically chosen locations is a crucial water management approach to alleviate flood risks and water scarcity. Selecting appropriate dam sites requires considering criteria such as precipitation, elevation, soil properties, slope, geomorphology, geology, lithology, stream order, distance from a road, and fault tectonics. To address this complex problem, integrating Multiple-Criteria Decision-Making (MCDM) techniques with Geographic Information System (GIS) has become increasingly prevalent. Among these techniques, the Analytic Hierarchy Process (AHP) is particularly effective for addressing water-related challenges. In this study, we developed a Dam Site Suitability Model (DSSM) by evaluating nine thematic layers: precipitation, stream order, geomorphology, geology, soil, elevation, slope, land use and land cover (LULC), and major fault tectonics. The AHP technique was employed to assign weights to these thematic layers, which were then used in an overlay analysis to create a suitability map with five classes ranging from high to low suitability. This study revealed that approximately 14% of the Purna sub-basin falls into the very high suitability category, while 27.2% is classified as highly suitable. This cost-effective approach not only simplifies the traditional method of dam site selection but also enhances decision-making accuracy. This methodology can be universally applied to identify potential dam sites, aiding flood mitigation and addressing water scarcity exacerbated by global and regional climate change. The DSSM, leveraging GIS and the AHP, can significantly improve dam management and promote sustainable, environmentally responsible water resource management practices worldwide. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

Supercritical open channel flows contribute substantially to the air–water transfer process in spillways, rivers, and streams. They are characterized by strong turbulent mixing and a substantial amount of air entrainment. The microscopic air–water properties in non-uniform self-aerated flows are investigated experimentally with various chute slopes, including air chord size and air–water transfer frequency. Microscopic air–water structures are primarily affected by chute slope, whereas the approach flow Reynolds number hardly influences them, resulting in self-similarity of the probability distribution of air chord length and air–water transfer frequency distribution in the self-aerated region. The distribution of bubble chord length is more continuous from the small to large scale in the high-air-concentration region for a greater chute slope, and the position of maximum air frequency moves to the higher-aeration zone and gets closely to the free surface. Moreover, empirical relationships are provided to predict the microscopic air–water properties in non-uniform self-aerated flows. Full article

The quality characteristics of runoff water during selected precipitation events in planted coniferous (C_P) and natural deciduous (D_N) forest stands in Pocheon-si, 27.0 km north of Seoul, were assessed via the mean event concentrations and discharge loads. The relationship between stream water quality and the runoff time differential (dQ/dt) indicated that the characteristics of the latter differed during the rising and falling stages of the two catchments. Pearson’s product moment correlation analysis revealed that chemical oxygen demand was significantly correlated with total organic carbon in the rising and falling limbs of the two catchments. When discharge loads were transported with actual precipitation events, the event load at the two sites increased with increasing discharge load. In particular, the total organic carbon and total nitrogen were higher in the C_P catchment than in the D_N catchment, whereas biological oxygen demand, total suspended solids, total nitrogen, and total phosphorus were higher in the D_N catchment than in the C_P catchment. Sequences of high and intense precipitation elevated discharge loads, with differences in loads related to the vegetation conditions in headwater areas (≤100 ha) with steep slopes (>20°) and narrow valleys. Full article

British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water Act. However, limited gauging of streams across the province poses challenges for ensuring water allocation while meeting Environmental Flow Needs. Overallocated watersheds and data-scarce watersheds in need of licensing highlight the need for robust streamflow prediction methods. To address these challenges, we developed a methodology that integrates the Drainage Area Ratio and Nearest Neighbors techniques to predict streamflows efficiently, without incurring additional financial costs. We utilized Digital Elevation Models and flow data from provincially and municipally managed hydrometric stations, as well as from the Water Survey of Canada, to normalize streamflows based on area, slope, and elevation. This approach ensures hydrological predictions that account for variability in hydrological processes resulting from differences in lumped-scale watershed characteristics. The method was validated using streamflow data from hydrometric stations maintained by the aforementioned entities. For validation, each station was iteratively treated as ungauged by temporarily removing it from the dataset and then predicting its streamflow using the proposed methodologies. The results demonstrated that the amalgamated Drainage Area Ratio–Nearest Neighbors approach outperformed the traditional Drainage Area Ratio method, offering reliable predictions for diverse watersheds. This study provides an adaptable and cost-effective framework for enhancing water resource management across BC. Full article

Climate changes have increased heavy rainfall, intensifying flood damage, especially along small streams with steep slopes, fast flows, and narrow widths. In Korea, nearly half of flood-related casualties occur in these regions, underscoring the need for effective flood early warning systems. However, predicting flood depths is challenging due to the complex channels and rapid flood wave propagation in small streams. This study developed a flood early warning framework (FEWF) tailored for small streams in Korea, optimizing rainfall–discharge nomographs using hydro-informatic data from four streams. The FEWF integrates a four-parameter logistic model with real-time updates with a nomograph using a robust constrained nonlinear optimization algorithm. A simplified two-level early warning system (attention and severe) is based on field-verified thresholds. Discharge predictions estimate the water depth in unmeasured cross-sections using the Manning formula, with real-time data updates allowing for the dynamic identification of the flood depth. The framework was validated during the 2022 flood event, where no inundation or bank failures were observed. By improving flood prediction and adaptive management, this framework can significantly enhance disaster response and reduce casualties in vulnerable small stream areas. Full article

Water, often described as the elixir of life, is a critical resource that sustains life on Earth. The acute water scarcity in the major basins of the Arabian Peninsula has been further aggravated by rapid population growth, urbanization, and the impacts of climate change. This situation underscores the urgent need for a comprehensive analysis of the region’s morphometric characteristics. Such an analysis is essential for informed decision-making in water resource management, infrastructure development, and conservation efforts. This study provides a foundational basis for implementing sustainable water management strategies and preserving ecological systems by deepening the understanding of the unique hydrological processes within the Arabian Peninsula. Additionally, this research offers valuable insights to policymakers for developing effective flood mitigation strategies by identifying vulnerable areas. The study focuses on an extensive investigation and assessment of morphometric parameters in the primary basins of the Arabian Peninsula, emphasizing their critical role in addressing water scarcity and promoting sustainable water management practices. The findings reveal that the Arabian Peninsula comprises 12 major basins, collectively forming a seventh-order drainage system and covering a total land area of 3.24 million km². Statistical analysis demonstrates a strong correlation between stream order and cumulative stream length, as well as a negative correlation between stream order and stream number (R² = 99%). Further analysis indicates that many of these basins exhibit a high bifurcation ratio, suggesting the presence of impermeable rocks and steep slopes. The hypsometric integral (HI) of the Peninsula is calculated to be 60%, with an erosion integral (EI) of 40%, indicating that the basin is in a mature stage of geomorphological development. Importantly, the region is characterized by a predominantly coarse drainage texture, limited infiltration, significant surface runoff, and steep slopes, all of which have critical implications for water resource management. Full article

This work combines geomorphological and historical research to decode the landscape in the world’s most famous painting: the Gioconda. The background of the painting was analysed in detail, and numerous morphological correspondences with the Montefeltro area in Central Italy were found. The upper valley of the Senatello stream features the Fumaiolo Massif, renowned for its springs that feed the River Tiber. The region is composed of the limestones and sandstones of the San Marino and Monte Fumaiolo Formations, alongside clay formations from the “Valmarecchia Nappe”. This lithological variety, the intense fracturing of the limestone rocks, and climatic and tectonic events during the Middle to Upper Pleistocene produced a complex and varied geomorphology. The landscape is marked by large landslides and significant debris deposits, reflecting its recent evolution. The painting, as well as historical documents and Leonardo’s drawings from his time in the Romagna region, provide evidence of a large lake beneath Mount Aquilone. The area was affected by a significant change in the morphology of the slopes, probably caused by a landslide that occurred in the period 1500–1700, a period characterised by climatic and tectonic upheavals, which may have led to the disappearance of the lake. Full article