4篇
您的检索式：作者名="Lima Neto E A"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Treatment of bone pain secondary to metastases using samarium-153-EDTMP 显示文摘 | Etchebehere E C Pereira Neto C A Lima M C | 2004 | SaoPauloMed2004，122，5： | 1 |
| 2 | Treatment of bone pain secondary to metastases using samarium153-EDTMP显示文摘 | Etchebehere E C Pereira Neto C A Lima M C | 2004 | Sao Paulo Med J2004，122，： | 1 |
| 3 | Centre and range method for fitting a linear regression model to symbolic interval data 显示文摘 | Lima Neto E A de Carvalho F A T | 2008 | Computational Statistics & Data Analysis2008，52，3： | 1 |
| 4 | Monthly and seasonal streamflow forecasting of large dryland catchments in Brazil显示文摘Streamflow forecasting in drylands is challenging.Data are scarce,catchments are highly humanmodified and streamflow exhibits strong nonlinear responses to rainfall.The goal of this study was to evaluate the monthly and seasonal streamflow forecasting in two large catchments in the Jaguaribe River Basin in the Brazilian semi-arid area.We adopted four different lead times:one month ahead for monthly scale and two,three and four months ahead for seasonal scale.The gaps of the historic streamflow series were filled up by using rainfall-runoff modelling.Then,time series model techniques were applied,i.e.,the locally constant,the locally averaged,the k-nearest-neighbours algorithm(k-NN)and the autoregressive(AR)model.The criterion of reliability of the validation results is that the forecast is more skillful than streamflow climatology.Our approach outperformed the streamflow climatology for all monthly streamflows.On average,the former was 25%better than the latter.The seasonal streamflow forecasting(SSF)was also reliable(on average,20%better than the climatology),failing slightly only for the high flow season of one catchment(6%worse than the climatology).Considering an uncertainty envelope(probabilistic forecasting),which was considerably narrower than the data standard deviation,the streamflow forecasting performance increased by about 50%at both scales.The forecast errors were mainly driven by the streamflow intra-seasonality at monthly scale,while they were by the forecast lead time at seasonal scale.The best-fit and worst-fit time series model were the k-NN approach and the AR model,respectively.The rainfall-runoff modelling outputs played an important role in improving streamflow forecasting for one streamgauge that showed 35%of data gaps.The developed data-driven approach is mathematical and computationally very simple,demands few resources to accomplish its operational implementation and is applicable to other dryland watersheds.Our findings may be part of drought forecasting systems and potentially help allocating water months in advance.Moreover,the developed strategy can serve as a baseline for more complex streamflow forecast systems. | Alexandre C COSTA Alvson B S ESTACIO Francisco de A de SOUZA FILHO Iran E LIMA NETO | 2021 | Journal of Arid Land2021，13，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员