Resultados de búsqueda - Random variables.

Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia por Leggesse, E. S., Derseh, W. A., Zimale, F. A., Tilahun, Seifu A., Meshesha, M. A.

“…As an alternative solution, this paper explores the use of machine learning (ML) techniques to map flood hazards based on readily available geo-environmental variables. We employed various ML classifiers, including decision tree (DT), random forest (RF), XGBoost (XGB), and k-nearest neighbor (kNN), to assess their performance in flood hazard mapping. …”
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Which machine learning algorithm is best suited for estimating reference evapotranspiration in humid subtropical climate? por Deb, P., Kumar, V., Urfels, A., Lautze, Jonathan F., Kamboj, B. R., Sharma, J. R., Yadav, S.

“…The sensitivity analysis of the input variables was followed by application of the best combination of variables in algorithm testing and training for generating ET0. …”
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Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens por Getachew, Fasil, Derks, M.F.L., Dessie, Tadelle, Hanotte, Olivier H., Barros, C.P., Crooijmans, R.P.M.A., Komen, H., Bastiaansen, J.W.M.

“…Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. …”
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Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa por Gachoki, S., Muthoni, F.K., Mahuku, G., Atehnkeng, J., Njeru, N., Kamau, J., Tripathi, L.

“…The trained random forest ranger model showed consistent performance with a balanced accuracy of 51%, overall accuracy of 48%, and F1-scores of 32%. …”
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Does small-scale irrigation affect women’s time allocation? Insights from Ethiopia por Lee, Yeyoung, Bryan, Elizabeth, Mason, Nicole M., Hassen, Ibrahim Worku, Theriault, Veronique, Ringler, Claudia

“…Two different approaches are used to address potential endogeneity issues related to time-constant and time-varying factors that may be correlated with both SSI and time use: an instrumental variables-correlated random effects approach and a fractional multinomial logit-correlated random effects with control function approach. …”
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A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia por Abera, Wuletawu, Tamene, Lulseged D., Tesfaye, Kindie, Jiménez, Daniel, Dorado, Hugo, Erkossa, Teklu, Kihara, Job Maguta, Ahmed, Jemal Seid, Amede, Tilahun, Ramírez Villegas, Julián Armando

“…For K and S, however, climatic variables played an important role alongside nutrient rates. …”
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Habitat preferences by wild boar Sus scrofa in southern Sweden based on clusters of GPS positions por Broberg, Emilia

“…There, the wild boar visited more areas with bushes and trees compared to random samples. Significant difference were also found between wild boar positions and random positions for mosses Bryophyta and Marchantiophyta, Wood-sorrel Oxalis acetosella and European beech Fagus sylvatica. …”

Forest structure and composition under contrasting precipitation regimes in the high mountains, Western Nepal por Bhatta, K.P., Aryal, A., Baral, H., Khanal, S., Acharya, A.K., Phomphakdy, C., Dorji, R.

“…This study strived to carry out a comparative evaluation of species diversity, main stand variables, and canopy cover of forests with contrasting precipitation conditions in the Annapurna range. …”
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Mapping land suitability for informal, small-scale irrigation development using spatial modelling and machine learning in the Upper East Region, Ghana por Akpoti, K., Higginbottom, T. P., Foster, T., Adhikari, R., Zwart, Sander J.

“…We assessed that 179,584 ± 49,853 ha is suitable for dry-season small-scale irrigation development when only biophysical variables are considered, and 158,470 ± 27,222 ha when socio-economic variables are included alongside the biophysical predictors, representing 77-89% of the current rainfed-croplands. …”
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Quantifying morphometric and adaptive characteristics of indigenous cattle genetic resources in northwest Ethiopia por Tenagne, A., Taye, M., Dessie, Tadelle, Muluneh, B., Kebede, D., Mekuriaw, Getinet

“…Multi-stage purposive and random sampling methods were employed to select the study areas, households and animals. …”
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Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots por Duque, Andres F., Patino, Diego, Colorado, Julian D., Petro, Eliel E., Rebolledo, Maria C, Mondragon, Iván Fernando, Espinosa, Natalia, Amézquita, Nelson, Puentes, Oscar D, Mendez, Diego, Jaramillo-Botero, Andres

“…In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. …”
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Discriminating Robusta coffee (Coffea canephora) cropping systems using leaf-level hyperspectral data por Kebede, G., Mudereri, B.T., Abdel-Rahman, E.M., Mutanga, O., Landmann, T., Odindi, J., Motisi, N., Pinard, F., Tonnang, H.E.Z.

“…The key to this process was the use of narrow spectral bands (NSBs) and various narrow-band vegetation indices (VIs), serving as predictor variables. A selection of critical variables (NSB = 9 and VIs = 8) was identified through the guided regularized random forest (RF) technique and then applied to four ML algorithms—RF, stochastic gradient boosting (GB), linear discriminant analysis, and support vector machine for classification experiments. …”
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Pathogen and pest communities in agroecosystems across climate gradients: anticipating future challenges in the highland tropics por Mouafo-Tchinda, R.A., Sula, A.I.P., Etherton, B.A., Okonya, J.S., Nakato, G.V., Xing, Y., Robledo, J., Adhikari, A., Blomme, G., Kantungeko, D., Nduwayezu, A., Kreuze. J.F., Kroschel, J., Legg, J.P., Garrett, K.A.

“…Host landscape variables were useful predictors for some species. …”
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Skattning av ålder och andra beståndsvariabler por Bredberg, Jonas

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Incidencia del capital social en el proceso de toma de decisiones comerciales en la producción ganadera = Impact of social capital on the business decision-making process in livest... por Ghiglione, Franco Alexis, Lema, Rolando Daniel, Ferro Moreno, Santiago, Braun, Rodolfo Oscar

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Economic valuation of restoring and conserving ecosystem services of Indian Sundarbans por Saha, D., Taron, Avinandan

“…Using contingent valuation, we seek to estimate the contribution forest fringe dwellers are ready to provide for restoration and conservation of the ecosystem services. Assuming a random utility framework, mean willingness to pay is estimated from the forest dwellers’ responses to the Dichotomous Choice bidding as well as open-ended bidding question using socio-economic variables which determine the value towards forest ecosystem services. …”
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Agricultural mechanisation and gendered labour activities across sectors: Micro-evidence from multi-country farm household data por Takeshima, Hiroyuki

“…This study aims to partly fill these knowledge gaps through micro‐evidence from seven countries (Ethiopia, Ghana, Nigeria, Tanzania, India, Nepal and Vietnam), using several nationally representative panel data and supplementary data and applying correlated random effects double‐hurdle models with instrumental variables. …”
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Cropland expansion links climate extremes and diets in Nigeria por Khan, B., Mehta, P., Wei, D., Ali, H., Adeluyi, O., Alabi, T.R., Olayide, O., Uponi, J.I., Davis, K.F.

“…Combining high-resolution data on forest cover and climate variables within random forest and panel regression models, we find that 25 to 31% of annual forest loss is linked to climate variability. …”
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Respuesta a la selección en festuca alta para suelo salino-sódico por Martinez, Emilce Soledad

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Agrobiodiversity and nutrition in traditional cropping systems - Homegardens of the indigenous Bakiga and Banyakole in southwestern Uganda. por Whitney, Cory W.

“…Households (n=102) were identified via stratified, random selection across three distinct regional ecological zones of southwestern Uganda (forest-edge, deforested areas, and wetland-edge). …”
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