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  1. 11
    دورية أكاديمية
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    دورية أكاديمية
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    كتاب
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    دورية أكاديمية
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    دورية أكاديمية

    المساهمون: fi=Vaasan yliopisto|en=University of Vaasa, orcid:0000-0003-4628-4486, fi=Tekniikan ja innovaatiojohtamisen yksikkö|en=School of Technology and Innovations, Digital Economy

    المصدر: Scopus:85173988169

    وصف الملف: fi=kokoteksti|en=fulltext; true

    العلاقة: Internet of Things; 24; https://doi.org/10.1016/j.iot.2023.100962Test; Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia; University of Vaasa; Academy of Finland; 445-9-495; https://osuva.uwasa.fi/handle/10024/16435Test; URN:NBN:fi-fe20231120147846

  6. 16
    دورية أكاديمية

    المساهمون: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials, Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports, Universitat Politècnica de València. Instituto Universitario de Ingeniería del Agua y del Medio Ambiente - Institut Universitari d'Enginyeria de l'Aigua i Medi Ambient, Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural - Escola Tècnica Superior d'Enginyeria Agronòmica i del Medi Natural, FUNDACION PRIMA, European Commission, GENERALITAT VALENCIANA

    العلاقة: Agricultural Water Management; info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//PROMETEO%2F2021%2F074//INtegrated FORecasting System for Water and the Environment (WATER4CAST)/; https://doi.org/10.1016/j.agwat.2023.108415Test; urn:issn:0378-3774; http://hdl.handle.net/10251/201350Test

  7. 17
    دورية أكاديمية

    المساهمون: FAPEMIG, CAPES, CNPQ

    المصدر: Brazilian Journal of Physical Geography; v. 16, n. 2 (2023): Revista Brasileira de Geografia Fisica; 1001-1012 ; Revista Brasileira de Geografia Física; v. 16, n. 2 (2023): Revista Brasileira de Geografia Fisica; 1001-1012 ; 1984-2295

    جغرافية الموضوع: Semiárido Brasileiro, Futuro

    وصف الملف: application/pdf

    العلاقة: https://periodicos.ufpe.br/revistas/rbgfe/article/view/256247/43770Test; https://periodicos.ufpe.br/revistas/rbgfe/article/downloadSuppFile/256247/42411Test; https://periodicos.ufpe.br/revistas/rbgfe/article/downloadSuppFile/256247/42412Test; Althoff, D., Bazame, H.C., Filgueiras, R., Dias, S.H.B., 2018. Heuristic methods applied in reference evapotranspiration modeling. Ciência e Agrotecnologia 42, 314–324. Althoff, D., Dias, S.H.B., Filgueiras, R., Rodrigues, L.N., 2020. ETo‐Brazil: A Daily Gridded Reference Evapotranspiration Data Set for Brazil (2000–2018). Water Resources Research 56, 1–24. https://doi.org/10.1029/2020WR027562Test. Breiman, L., 2001. Random forests. Machine learning 45, 5–32. Burrell, A.L., Evans, J.P., De Kauwe, M.G., 2020. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat Commun 11, 3853. https://doi.org/10.1038/s41467-020-17710-7Test Castro Oliveira, G., Arruda, D.M., Fernandes Filho, E.I., Veloso, G.V., Francelino, M.R., Schaefer, C.E.G.R., 2021. Soil predictors are crucial for modelling vegetation distribution and its responses to climate change. Science of The Total Environment 146680. https://doi.org/10.1016/j.scitotenv.2021.146680Test. Costa, J.F.C.B., Silva, R.M., Santos, C.A.G., Silva, A.M., Vianna, P.C.G., 2021. Analysis of the response of the Epitácio Pessoa reservoir (Brazilian semi-arid region) to potential future drought, water transfer and LULC scenarios. Natural Hazards 1–25. https://dx.doi.org/10.1007/s11069-021-04736-3Test. Cunha, A.P.M., Alvalá, R.C., Nobre, C.A., Carvalho, M.A., 2015. Monitoring vegetative drought dynamics in the Brazilian semi-arid region. Agricultural and forest meteorology 214, 494–505. https://doi.org/10.1016/j.agrformet.2015.09.010Test. Del Cerro, R.T.G., Subathra, M.S.P., Kumar, N.M., Verrastro, S., George, S.T., 2021. Modelling the daily reference evapotranspiration in semi-arid region of South India: A case study comparing ANFIS and empirical models. Information Processing in Agriculture 8, 173–184. https://dx.doi.org/10.1016/j.inpa.2020.02.003Test. Dias, H.B., Sentelhas, P.C., 2021. Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil. International Journal of Biometeorology 1–13. https://dx.doi.org/10.1007/s00484-021-02145-6Test. Dias, S.H.B., Filgueiras, R., Fernandes Filho, E.I., Arcanjo, G.S., Silva, G.H. da, Mantovani, E.C., Cunha, F.F. da, 2021. Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing. Plos one 16, e0245834. https://dx.doi.org/10.1371/journal.pone.0245834Test. Fan, J., Wu, L., Zhang, F., Xiang, Y., Zheng, J., 2016. Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. Journal of Hydrology 542, 923–937. https://doi.org/10.1016/j.jhydrol.2016.09.060Test. Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315. Gao, W., Zheng, C., Liu, X., Lu, Y., Chen, Y., Wei, Y., Ma, Y., 2022. NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China. Ecological Indicators 137, 108745. Gomes, L.C., Faria, R.M., Souza, E., Veloso, G.V., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2019. Modelling and mapping soil organic carbon stocks in Brazil. Geoderma 340, 337–350. https://doi.org/10.1016/j.geoderma.2019.01.007Test Grünzweig, J.M., Boeck, H.J., Rey, A., Santos, M.J., Adam, O., Bahn, M., Belnap, J., Deckmyn, G., Dekker, S.C., Flores, O., 2022. Dryland mechanisms could widely control ecosystem functioning in a drier and warmer world. Nature Ecology & Evolution 1–13. Hausfather, Z., Marvel, K., Schmidt, G.A., Nielsen-Gammon, J.W., Zelinka, M., 2022. Climate simulations: Recognize the ‘hot model’problem. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society 25, 1965–1978. https://doi.org/10.1002/joc.1276Test. Houborg, R., McCabe, M.F., 2018. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS Journal of Photogrammetry and Remote Sensing 135, 173–188. Huang, J., Ji, M., Xie, Y., Wang, S., He, Y., Ran, J., 2016. Global semi-arid climate change over last 60 years. Climate Dynamics 46, 1131–1150. https://doi.org/10.1007/s00382-015-2636-8Test. Jiang, F., Xie, X., Liang, S., Wang, Y., Zhu, B., Zhang, X., Chen, Y., 2021. Loess Plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration. Agricultural and Forest Meteorology 311, 108669. Kang, Z., Qiu, B., Xiang, Z., Liu, Y., Lin, Z., Guo, W., 2022. Improving simulations of vegetation dynamics over the Tibetan Plateau: Role of atmospheric forcing data and spatial resolution. Advances in Atmospheric Sciences 39, 1115–1132. Kim, J., Choi, J., Choi, C., Park, S., 2013. Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Science of the Total Environment 452, 181–195. https://doi.org/10.1016/j.scitotenv.2013.02.005Test. Kuhn, M., Johnson, K., 2013. Applied predictive modeling. Springer. https://dx.doi.org/10.1007/978-1-4614-6849-3Test. Kuhn, M., Quinlan, R., 2018. Cubist: Rule-and instance-based regression modeling. R package version 0.2. 2. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., Team, R.C., 2020. Package ‘caret.’ The R Journal 223, 7. Leal Filho, W., Totin, E., Franke, J.A., Andrew, S.M., Abubakar, I.R., Azadi, H., Nunn, P.D., Ouweneel, B., Williams, P.A., Simpson, N.P., 2022. Understanding responses to climate-related water scarcity in Africa. Science of the Total Environment 806, 150420. Li, M., Chu, R., Sha, X., Islam, A.R.M.T., Jiang, Y., Shen, S., 2022. How Has the Recent Climate Change Affected the Spatiotemporal Variation of Reference Evapotranspiration in a Climate Transitional Zone of Eastern China? ISPRS International Journal of Geo-Information 11, 300. Liu, Y., Yao, X., Wang, Q., Yu, J., Jiang, Q., Jiang, W., Li, L., 2021. Differences in reference evapotranspiration variation and climate-driven patterns in different altitudes of the Qinghai–Tibet plateau (1961–2017). Water 13, 1749. Marengo, J.A., Galdos, M.V., Challinor, A., Cunha, A.P., Marin, F.R., Vianna, M. dos S., Alvala, R.C., Alves, L.M., Moraes, O.L., Bender, F., 2022. Drought in Northeast Brazil: A review of agricultural and policy adaptation options for food security. Climate Resilience and Sustainability 1, e17. Milborrow, S., Tibshirani, R., 2019. Package ‘earth’: Multivariate Adaptive Regression Splines. Nooni, I.K., Hagan, D.F.T., Wang, G., Ullah, W., Lu, J., Li, S., Dzakpasu, M., Prempeh, N.A., Lim Kam Sian, K.T., 2021. Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6. International Journal of Environmental Research and Public Health 18, 6760. Núñez-López, J.M., Cansino-Loeza, B., Sánchez-Zarco, X.G., Ponce-Ortega, J.M., 2022. Involving resilience in assessment of the water–energy–food nexus for arid and semi-arid regions. Clean Technologies and Environmental Policy 1–13. Oliver, M.A., Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3), 313-332. Orimoloye, I.R., Belle, J.A., Orimoloye, Y.M., Olusola, A.O., Ololade, O.O., 2022. Drought: A common environmental disaster. Atmosphere 13, 111. Pielke Jr, R., Burgess, M.G., Ritchie, J., 2022. Plausible 2005–2050 emissions scenarios project between 2° C and 3° C of warming by 2100. Environmental Research Letters 17, 024027. Rodriguez, P.P., Gianola, D., 2016. BRNN: Bayesian regularization for feed-forward neural networks. R package version 0.6. Salas-Martínez, F., Valdés-Rodríguez, O.A., Palacios-Wassenaar, O.M., Márquez-Grajales, A., 2021. Analysis of the Evolution of Drought through SPI and Its Relationship with the Agricultural Sector in the Central Zone of the State of Veracruz, Mexico. Agronomy 11, 2099. Santos, T.G., Battisti, R., Casaroli, D., Alves, J., Evangelista, A.W.P., 2021. Assessment of agricultural efficiency and yield gap for soybean in the Brazilian Central Cerrado biome. Bragantia 80, 1–11. https://doi.org/10.1590/1678-4499.20200352Test. Scott, R.L., Biederman, J.A., Hamerlynck, E.P., Barron‐Gafford, G.A., 2015. The carbon balance pivot point of southwestern US semi-arid ecosystems: Insights from the 21st century drought. Journal of Geophysical Research: Biogeosciences 120, 2612–2624. Shi, L., Feng, P., Wang, B., Liu, D.L., Yu, Q., 2020. Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models. Journal of Hydrology 590, 125394. https://doi.org/10.1016/j.jhydrol.2020.125394Test Silveira, S.M.B., Silva, M.G., 2019. Conflitos socioambientais por água no Nordeste brasileiro: expropriações contemporâneas e lutas sociais no campo. Revista Katálysis 22, 342–352. Souza, C.M.P., Veloso, G.V., Mello, C.R., Ribeiro, R.P., Silva, L.A.P., Leite, M.E., Fernandes Filho, E.I., 2022. Spatiotemporal prediction of rainfall erosivity by machine learning in southeastern Brazil. Geocarto International 1–19. https://doi.org/10.1080/10106049.2022.2060318Test Silva, C., Teixeira, A.C., Manzione, R., 2020. Utilização de Redes Neurais com Regularização Bayesiana na Modelagem de Evapotranspiração de Referência em Agroecossistemas Semiáridos. Revista Brasileira de Engenharia de Biossistemas 14, 73–84. Van Vuuren, D.P., Riahi, K., Calvin, K., Dellink, R., Emmerling, J., Fujimori, S., Kc, S., Kriegler, E., O’Neill, B., 2017. The Shared Socio-economic Pathways: Trajectories for human development and global environmental change. Global Environmental Change 42, 148–152. https://doi.org/10.1016/j.gloenvcha.2016.10.009Test Wendt, K.A., Häuselmann, A.D., Fleitmann, D., Berry, A.E., Wang, X., Auler, A.S., Cheng, H., Edwards, R.L., 2019. Three-phased Heinrich Stadial 4 recorded in NE Brazil stalagmites. Earth and Planetary Science Letters 510, 94–102. https://doi.org/10.1016/j.epsl.2018.12.025Test.; Wu, T., Zhang, W., Jiao, X., Guo, W., Hamoud, Y.A., 2021. Evaluation of stacking and blending ensemble learning methods for estimating daily reference evapotranspiration.Computers and Electronics in Agriculture 184, 106039. https://doi.org/10.1016/j.compag.2021.10603Test Xavier, A.C., King, C.W., Scanlon, B.R., 2016. Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 36, 2644–2659. https://doi.org/10.1002/joc.4518Test. Xing, X., Qian, J., Chen, X., Chen, C., Sun, J., Wei, S., Yimamaidi, D., Zhanar, Z., 2022. Analysis of Effects of Recent Changes in Hydrothermal Conditions on Vegetation in Central Asia. Land 11, 327. Yoo, J., Kwon, H.-H., Lee, J.-H., Kim, T.-W., 2016. Influence of evapotranspiration on future drought risk using bivariate drought frequency curves. KSCE Journal of Civil Engineering 20, 2059–2069. https://doi.org/10.1007/s12205-015-0078-9Test. Zhang, G., Gan, T.Y., Su, X., 2022. Twenty-first century drought analysis across China under climate change. Climate Dynamics 59, 1665–1685.; https://periodicos.ufpe.br/revistas/rbgfe/article/view/256247Test

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    دورية أكاديمية
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    دورية أكاديمية
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    دورية أكاديمية

    المصدر: ReTII; Vol 18 No 1 (2023): Prosiding Seminar Nasional ReTII ke-18 (Edisi Penelitian); 155-159 ; 1907-5995

    وصف الملف: application/pdf

    العلاقة: //journal.itny.ac.id/index.php/ReTII/article/view/4116/1681; http://journal.itny.ac.id/index.php/ReTII/article/view/4116Test