000204237 001__ 204237
000204237 005__ 20190509132517.0
000204237 0247_ $$2doi$$a10.5075/epfl-lchcomm-60
000204237 022__ $$a1661-1179
000204237 037__ $$aBOOK
000204237 245__ $$aHydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques
000204237 269__ $$a2014
000204237 260__ $$bEPFL - LCH$$c2014$$aLausanne
000204237 336__ $$aBooks
000204237 490__ $$aCommunication (Laboratoire de constructions hydrauliques, Ecole polytechnique fédérale de Lausanne)$$v60
000204237 520__ $$aThe development of hydraulic-hydrologic models is a challenge in the case of large catchment areas with scarce or erroneous measurement data and observations. With his study Mr. Dr. José Pedro Matos made several original contributions in order to overcome this challenge. The scientific developments were applied at Zambezi River basin in Africa in the framework of the interdisciplinary African Dams research project (ADAPT). First of all, procedures and selection criteria for satellite data regarding topography, rainfall, land use, soil types and cover had to be developed. With the goal to extend the time scope of the analysis, Dr. Matos introduced a novel Pattern-Oriented Memory (POM) historical rainfall interpolation methodology. When POM interpolated rainfall is applied to hydrologic models it effectively opens up new possibilities related to extended calibration and the simulation of historical events, which would otherwise be difficult to exploit. A new scheme for rainfall aggregation was proposed, based on hydraulic considerations and easily implemented resorting to remote sensing data, which was able to enhance forecasting results. Dr. Matos used machine-learning models in an innovative way for discharge forecast. He compared the alternative models (e.g. Autoregressive Moving-Average (ARMA), Artificial Neural Networks (ANN) and Support-Vector Regression (SVR)). Dr. Matos made then significant contributions to the enhancement of rainfall aggregation techniques and the study of limitations inherent to SVR forecasting model. He proposed also a novel method for developing empirical forecast probability distributions. Finally Dr. Matos could successfully calibrate, probably for the first time, a daily hydrological model covering the whole Zambezi River basin (ZRB).
000204237 6531_ $$ahydraulic-hydrologic models
000204237 6531_ $$alarge catchment areas
000204237 6531_ $$aZambezi River basin
000204237 6531_ $$aAfrican Dams research project
000204237 6531_ $$aADAPT
000204237 6531_ $$aPattern-Oriented Memory
000204237 6531_ $$aPOM historical rainfall interpolation
000204237 6531_ $$ainterpolated rainfall
000204237 6531_ $$amachine-learning models
000204237 700__ $$0242674$$g198198$$aGamito de Saldanha Calado Matos, José Pedro
000204237 720_1 $$aSchleiss, Anton$$eed.$$g112841$$0241228
000204237 8564_ $$uhttps://infoscience.epfl.ch/record/204237/files/Comm_LCH_60.pdf$$zn/a$$s17776954
000204237 8564_ $$uhttps://infoscience.epfl.ch/record/204237/files/Thumb_Comm_LCH_60.png$$zn/a$$s19536$$yn/a
000204237 909C0 $$xU10263$$0252079$$pLCH
000204237 909C0 $$xU10263$$0255473$$pPL-LCH
000204237 909CO $$qDOI2$$qGLOBAL_SET$$pbook$$pDOI$$pENAC$$ooai:infoscience.tind.io:204237
000204237 917Z8 $$x246105
000204237 937__ $$aEPFL-BOOK-204237
000204237 973__ $$sPUBLISHED$$aEPFL
000204237 980__ $$aBOOK