3 research outputs found
A machine learning framework for multi-hazards modeling and mapping in a mountainous area
No full textA Hot-Spot Analysis of Forest Roads Based on Soil Erosion and Sediment Production
No full textForest roads have been recognized as one of the significant contributors to soil erosion processes in forested areas. The construction and maintenance of forest roads can cause severe environmental impacts, including soil erosion, sedimentation, and degradation of aquatic ecosystems. The main objective of the present study is to analyze the impact of forest road networks on soil erosion and sedimentation in the context of the Zagros forestlands, Iran. This study aims to assess the soil erosion and sedimentation on forest roads in four case studies in the Zagros forestlands. This study collected data using field surveys and SEDMODL equations to determine input factors and sedimentation and erosion rates. This study found that roadside erosion is strongly correlated with geological factors, road width, and precipitation factors. The height changes of 144 benchmarks were recorded during one study year (2021–2022) on four study roads, and the measured results of erosion benchmarks indicated an average soil erosion of 3, 2.6, 4.7, and 3.5 mm per year around the Bideleh, Kohian, Nazi, and Tabarak roads, respectively. This study measured soil erosion and sedimentation at three distances (5, 15, and 25 m) from the road, and found a significant difference in the height changes of the benchmarks at varying distances from the study roads. A hot-spot analysis was conducted using GIS 10.8, and the results indicated that a significant portion of the studied forest roads had very high erosion production and hot spots. The results of the hot-spot analysis indicated that 30.8%, 22.6%, 39.8%, and 14.5% of the study forest roads, Nazi, Tabarak, Bideleh, and Kohian roads, respectively, are identified as areas with very high erosion production and hot spots. These results highlight the need for effective management strategies to minimize the impact of erosion on road infrastructure and the surrounding environment. Overall, this study provides important insights into the soil erosion and sedimentation on forest roads, and the findings presented here can be used to inform future road construction and maintenance
Representaciones de patrones para la clasificación de datos (no-)vectoriales (no-)métricos con aplicaciones en el Monitoreo de Salud Estructural y la ingeniería de amenazas geotécnicas/naturales
No full textgráficos, tablasNowadays, data-driven modelling in structural and geo-engineering problems using Statistical Pattern Recognition and Machine Learning provides powerful and more versatile tools within a predictive framework. In contrast to the mainstream orientations of the state-of-art in data-driven structural and geo-engineering surrogates, which are based on advanced and (hyper-)parametrized classifiers, this thesis is focused on data representation issues. Firstly, for vectorial slope/landslide data, feature-based vector spaces are enriched and enhanced according to the Occam’s razor principle, which is achieved through three simple but powerful existing variants of a transparent classifier as the nearest neighbor rule. Secondly, for non-vectorial SHM data, powerful and highly discriminant dissimilarity-vector spaces are built-up using spectral/time-frequency information from structural states, adopting a
proximity-based learning scheme. In both cases, the results show the importance of a proper data representation and its key role in a bottom-up design for surrogate modelling. (Texto tomado de la fuente)Actualmente, el Reconocimiento de Patrones Estadístico y el Aprendizaje de Máquinas proveen herramientas poderosas y versátiles para el modelamiento predictivo de problemas de estructuras civiles, mecánicas y de la geo-ingeniería. A diferencia de las principales tendencias en el estado del arte en los sustitutos basados en datos en problemas de estructuras y de geo-ingeniería, esta tesis se enfoca en la representación de los datos. Primero, para datos vectoriales de taludes/deslizamientos, los espacios vectoriales basados en características son enriquecidos y mejorados de acuerdo al principio de la navaja de Occam o de parsimonia, el cual se logra mediante tres simples pero poderosos variantes ya existentes del clasificador de vecinos más cercanos. Segundo, para datos no-vectoriales pertenecientes al Monitoreo de Salud Estructural, son construidos, poderosos y altamente discriminantes, espacios de disimilitudes usando información espectral/tiempo-frecuencia, tomando un esquema de aprendizaje basado en proximidades. En ambos casos, los resultados demuestran la importancia de una apropiada representación de datos y su influencia en el diseño incremental de modelos sustitutos.DoctoradoDoctor en IngenieríaStatistical Pattern Recognition, Machine Learning and Signal ProcessingEléctrica, Electrónica, Automatización Y Telecomunicacione
