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Anthony E. Akpan, Mahesh Narayanan, and T. Harinarayana

; Spichak et al. 2011 ). To make reliable predictions, the requisite condition that the ANN method needs while solving complicated problems is to have a large volume of input–output data pairs for it to use in training the network instead of mathematical equations and other empirical relations ( Ali Akcayol and Cinar 2005 ). Currently, the ANN technique is the most popular artificial learning tool in the geosciences, with applications including automatic seismic wave arrival time picking ( Dai and

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Charles P-A. Bourque and Quazi K. Hassan

wetness index ( Moore et al. 1993 ). Net radiation from the radiation module ( Bourque and Gullison 1998 ; Bourque et al. 2000 ) is used to assess potential ET, from which actual ET is calculated based on a Newton–Raphson solution of SWC ( Moore et al. 1993 ). Deep seepage is computed for saturated areas. Gridded output from the individual modules of LanDSET can be viewed and further processed in GIS. The reader is encouraged to consult the papers cited for further information concerning LanDSET

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Paul A. T. Higgins and Jonah V. Steinbuck

and nonexperts to explore the potential consequences of climate change. This new conceptual tool is designed to 1) be sufficiently intuitive to enable virtually anyone to understand, evaluate, and use; 2) make necessary assumptions and choices explicit; 3) minimize the use of weakly supported assumptions whenever possible; and 4) be easy to update and refine as new information becomes available (either with increasing learning by the tool user or through scientific advances). Based on these four

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Xuebin Yang

(MODIS) and Satellite pour l’Observation de la Terre (SPOT)–Vegetation (VGT) data. They mainly take advantage of the different phenophases between woody plants and herbaceous vegetation. However, this approach requires deep prior knowledge about the vegetation phenology of the study area. Moreover, the phenology of woody plants or herbaceous vegetation in other savanna areas may not be as uniform as that in the Sahel area. Yang and Crews (2019b) mapped the fractional woody plant cover of Texas

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Bryan Pijanowski, Nathan Moore, Dasaraden Mauree, and Dev Niyogi

node ( Figure 2b ). Weights are then applied to a nonlinear activation function at each node ( Figure 2c ), which is then integrated across all nodes to estimate the output. A root-mean-square error (RSME) of the estimate and observed output value for all locations is then calculated ( Figure 2a ); a full pass forward using weights and then back-propagating errors is called a cycle and the process of learning about patterns in data is called training. The RSME is then compared to the previous cycle

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Hal F. Needham and Barry D. Keim

2013 ) and Charley was not even centered over the Gulf of Mexico 18 h before landfall. Some may argue that Charley generated a relatively small surge because the bathymetry off the coast of southwest Florida is too deep to enable hurricanes to produce high storm surges in this region. However, other coastal flooding events in this region prove that relatively high storm surge levels can be reached in southwest Florida. In 1992, Hurricane Andrew generated a 4-m storm tide at North Highland Beach

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Shuguang Liu, Ben Bond-Lamberty, Lena R. Boysen, James D. Ford, Andrew Fox, Kevin Gallo, Jerry Hatfield, Geoffrey M. Henebry, Thomas G. Huntington, Zhihua Liu, Thomas R. Loveland, Richard J. Norby, Terry Sohl, Allison L. Steiner, Wenping Yuan, Zhao Zhang, and Shuqing Zhao

cover on the development of deep convection . J. Appl. Meteor. , 34 , 2029 – 2045 , doi: 10.1175/1520-0450(1995)034<2029:NSOTEO>2.0.CO;2 . 10.1175/1520-0450(1995)034<2029:NSOTEO>2.0.CO;2 Clark , M. P. , A. G. Slater , D. E. Rupp , R. A. Woods , J. A. Vrugt , H. V. Gupta , T. Wagener , and L. E. Hay , 2008 : Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models . Water Resour. Res. , 44 , W00B02

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