@article{fdi:010086666,
  title = {{S}mooth spatial modeling of extreme {M}editerranean precipitation},
  author = {{H}ammami, {H}ela and {C}arreau, {J}ulie and {N}eppel, {L}. and {E}lasmi, {S}. and {F}eki, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{E}xtreme precipitation events can lead to disastrous floods, which are the most significant natural hazards in the {M}editerranean regions. {T}herefore, a proper characterization of these events is crucial. {E}xtreme events defined as annual maxima can be modeled with the generalized extreme value ({GEV}) distribution. {O}wing to spatial heterogeneity, the distribution of extremes is non-stationary in space. {T}o take non-stationarity into account, the parameters of the {GEV} distribution can be viewed as functions of covariates that convey spatial information. {S}uch functions may be implemented as a generalized linear model ({GLM}) or with a more flexible non-parametric non-linear model such as an artificial neural network ({ANN}). {I}n this work, we evaluate several statistical models that combine the {GEV} distribution with a {GLM} or with an {ANN} for a spatial interpolation of the {GEV} parameters. {K}ey issues are the proper selection of the complexity level of the {ANN} (i.e., the number of hidden units) and the proper selection of spatial covariates. {T}hree sites are included in our study: a region in the {F}rench {M}editerranean, the {C}ap {B}on area in northeast {T}unisia, and the {M}erguellil catchment in central {T}unisia. {T}he comparative analysis aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.},
  keywords = {intense precipitation events ; non-stationarity in space ; generalized ; extreme value distribution ; spatial interpolation ; generalized linear ; models ; artificial neural networks ; {TUNISIE} ; {ZONE} {MEDITERRANEENNE}},
  booktitle = {},
  journal = {{W}ater},
  volume = {14},
  numero = {22},
  pages = {3782 [18 p.]},
  year = {2022},
  DOI = {10.3390/w14223782},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086666},
}