Examinando por Materia "Random forest"

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Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12) Pizarro Carcausto, Samuel Edwin; Pricope , Narcisa G.; Vera Vilchez, Jesús Emilio; Cruz Luis, Juancarlos Alejandro; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
The distribution of cadmium in soil and cacao beans in Peru
(Elsevier, 2023-04-11) Thomas, Evert; Atkinson, Rachel; Zavaleta, Diego; Rodriguez, Carlos; Lastra Paucar, Sphyros Roomel-Luciano; Yovera, Fredy; Arango, Karina; Pezo, Abel; Aguilar Zapata, Javier Neptali; Tames, Miriam; Ramos, Ana; Cruz, Wilbert; Cosme, Roberto; Espinoza, Eduardo; Chavez, Carmen Rosa; Ladd, Brenton
Peru is the eighth largest producer of cacao beans globally, but high cadmium contents are constraining access to international markets which have set upper thresholds for permitted concentrations in chocolate and derivatives. Preliminary data have suggested that high cadmium concentrations in cacao beans are restricted to specific regions in the country, but to date no reliable maps exist of expected cadmium concentrations in soils and cacao beans. Drawing on >2000 representative samples of cacao beans and soils we developed multiple national and regional random forest models to develop predictive maps of cadmium in soil and cacao beans across the area suitable for cacao cultivation. Our model projections show that elevated concentrations of cadmium in cacao soils and beans are largely restricted to the northern parts of the country in the departments of Tumbes, Piura, Amazonas and Loreto, as well as some very localized pockets in the central departments of Huánuco and San Martin. Unsurprisingly, soil cadmium was the by far most important predictor of bean cadmium. Aside from the south-eastern to north-western spatial trend of increasing cadmium values in soils and beans, the most important predictors of both variables in nation-wide models were geology, rainfall seasonality, soil pH and rainfall. At regional level, alluvial deposits and mining operations were also associated with higher cadmium levels in cacao beans. Based on our predictive map of cadmium in cacao beans we estimate that while at a national level <20 % of cacao farming households might be impacted by the cadmium regulations, in the most affected department of Piura this could be as high as 89 %.
From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach
(Springer, 2024-09-09) Carbajal, Mariella; Ramirez, David A.; Turin Canchaya, Cecilia Claudia; Schaeffer, Sean M.; Konkel, Julie; Ninanya, Johan; Rinza, Javier; De Mendiburu, Felipe; Zorogastua, Percy; Villaorduña, Liliana; Quiroz, Roberto
Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (d13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (‘‘bofedales’’) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC prediction, whereas ANN was the best to model RSOC. ‘‘Bofedales’’ showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted d13CSOC (- 27.0 ± 0.44 &) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of ‘‘bofedales’’ as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and d13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.