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Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya
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Bester Preis: Fr. 42.47 (€ 43.52)¹ (vom 01.06.2019)Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya (Paperback) (2019)
ISBN: 9783668917484 bzw. 3668917485, vermutlich in Englisch, GRIN Verlag, Germany, Taschenbuch, neu.
Language: English. Brand new Book. Doctoral Thesis / Dissertation from the year 2016 in the subject Engineering - Civil Engineering, grade: 80.0, Egerton University, course: AGRICULTURAL ENGINEERING, language: English, abstract: Drought is a critical stochastic natural disaster that adversely affects water resources, ecosystems and people. Drought is a condition characterized by scarcity of precipitation and/or water quantity that negatively affects the global, regional and local land-scales. At both global and regional scales, drought frequency and severity have been increasing leading to direct and indirect decline in water resources. Increase in drought severity and frequency in the upper Tana River basin, Kenya, water resources systems have been adversely affected. Timely detection and forecasting of drought is crucial in planning and management of water resources. The main objective of this research was to formulate the most appropriate models for assessment and forecasting of drought using Indices and Artificial Neural Networks (ANNs) for the basin. Hydro-meteorlogical data for the period 1970-2010 at sixteen hydrometric stations was used to test the performance of the indices in forecasting of the future drought at 1, 3, 6, 9, 12, 18 and 24-months lead times, by constructing ANN models with different time delays. Drought conditions at monthly temporal resolution were evaluated using selected drought indices. The occurrence of drought was investigated using non-parametric Man-kendall trend test. Spatial distribution of drought severity was determined using Kriging interpolation techinique. In addition, a standard Nonlinear-Integrated Drought Index (NDI), for drought forecasting in the basin was developed using hydro-meteoroogical data for the river basin. The results of spaial drought show that the south-eastern parts of the basin are more prone to drought risks than the north-western areas. The Mann-Kendall trend test indicates an increasing drought trend in the south-eastern and no trend in north-western areas of the basin. Development of Surface Water Supply Index (SWSI) function, NDI and characteristic curves defining the return period and the probability of different drought magnitudes based on Drought Indices (DIs) was achieved. Drought Severity-Duration-Frequency (SDF) curves were developed. The formulated NDI tool can be adopted for a synchronized assessment and forecasting of all the three operational drought types in the basin. The results can be used in assisting water resources managers for timely detection and forecasting of drought conditions in prioritized planning of drought preparedness and early warning systems.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya (2016)
ISBN: 9783668917484 bzw. 3668917485, vermutlich in Englisch, Grin Verlag, Taschenbuch, neu.
Von Händler/Antiquariat, buecher.de GmbH & Co. KG, [1].
Doctoral Thesis / Dissertation from the year 2016 in the subject Engineering - Civil Engineering, grade: 80.0, Egerton University, course: AGRICULTURAL ENGINEERING, language: English, abstract: Drought is a critical stochastic natural disaster that adversely affects water resources, ecosystems and people. Drought is a condition characterized by scarcity of precipitation and/or water quantity that negatively affects the global, regional and local land-scales. At both global and regional scales, drought frequency and severity have been increasing leading to direct and indirect decline in water resources. Increase in drought severity and frequency in the upper Tana River basin, Kenya, water resources systems have been adversely affected. Timely detection and forecasting of drought is crucial in planning and management of water resources. The main objective of this research was to formulate the most appropriate models for assessment and forecasting of drought using Indices and Artificial Neural Networks (ANNs) for the basin. Hydro-meteorlogical data for the period 1970-2010 at sixteen hydrometric stations was used to test the performance of the indices in forecasting of the future drought at 1, 3, 6, 9, 12, 18 and 24-months lead times, by constructing ANN models with different time delays. Drought conditions at monthly temporal resolution were evaluated using selected drought indices. The occurrence of drought was investigated using non-parametric Man-kendall trend test. Spatial distribution of drought severity was determined using Kriging interpolation techinique. In addition, a standard Nonlinear-Integrated Drought Index (NDI), for drought forecasting in the basin was developed using hydro-meteoroogical data for the river basin. The results of spaial drought show that the south-eastern parts of the basin are more prone to drought risks than the north-western areas. The Mann-Kendall trend test indicates an increasing drought trend in the south-eastern and no trend in north-western areas of the basin. Development of Surface Water Supply Index (SWSI) function, NDI and characteristic curves defining the return period and the probability of different drought magnitudes based on Drought Indices (DIs) was achieved. Drought Severity-Duration-Frequency (SDF) curves were developed. The formulated NDI tool can be adopted for a synchronized assessment and forecasting of all the three operational drought types in the basin. The results can be used in assisting water resources managers for timely detection and forecasting of drought conditions in prioritized planning of drought preparedness and early warning systems. 2019. 244 S. 26 Farbabb. 210 mm Versandfertig in 6-10 Tagen, Softcover, Neuware, offene Rechnung (Vorkasse vorbehalten).
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya (2019)
ISBN: 9783668917484 bzw. 3668917485, in Englisch, 244 Seiten, GRIN Verlag, Taschenbuch, neu.
Von Händler/Antiquariat, Books on Demand GmbH.
Doctoral Thesis / Dissertation from the year 2016 in the subject Engineering - Civil Engineering, grade: 80.0, Egerton University, course: AGRICULTURAL ENGINEERING, language: English, abstract: Drought is a critical stochastic natural disaster that adversely affects water resources, ecosystems and people. Drought is a condition characterized by scarcity of precipitation and/or water quantity that negatively affects the global, regional and local land-scales. At both global and regional scales, drought frequency and severity have been increasing leading to direct and indirect decline in water resources. Increase in drought severity and frequency in the upper Tana River basin, Kenya, water resources systems have been adversely affected. Timely detection and forecasting of drought is crucial in planning and management of water resources. The main objective of this research was to formulate the most appropriate models for assessment and forecasting of drought using Indices and Artificial Neural Networks (ANNs) for the basin. Hydro-meteorlogical data for the period 1970-2010 at sixteen hydrometric stations was used to test the performance of the indices in forecasting of the future drought at 1, 3, 6, 9, 12, 18 and 24-months lead times, by constructing ANN models with different time delays. Drought conditions at monthly temporal resolution were evaluated using selected drought indices. The occurrence of drought was investigated using non-parametric Man-kendall trend test. Spatial distribution of drought severity was determined using Kriging interpolation techinique. In addition, a standard Nonlinear-Integrated Drought Index (NDI), for drought forecasting in the basin was developed using hydro-meteoroogical data for the river basin. The results of spaial drought show that the south-eastern parts of the basin are more prone to drought risks than the north-western areas. The Mann-Kendall trend test indicates an increasing drought trend in the south-eastern and no trend... Taschenbuch, Label: GRIN Verlag, GRIN Verlag, Produktgruppe: Book, Publiziert: 2019-02-06, Studio: GRIN Verlag.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya (2016)
ISBN: 9783668917484 bzw. 3668917485, vermutlich in Englisch, neu, Hörbuch.
Doctoral Thesis / Dissertation from the year 2016 in the subject Engineering - Civil Engineering, grade: 80.0, Egerton University, course: AGRICULTURAL ENGINEERING, language: English, abstract: Drought is a critical stochastic natural disaster that adversely affects water resources, ecosystems and people. Drought is a condition characterized by scarcity of precipitation and/or water quantity that negatively affects the global, regional and local land-scales. At both global and regional scales, drought frequency and severity have been increasing leading to direct and indirect decline in water resources. Increase in drought severity and frequency in the upper Tana River basin, Kenya, water resources systems have been adversely affected. Timely detection and forecasting of drought is crucial in planning and management of water resources. The main objective of this research was to formulate the most appropriate models for assessment and forecasting of drought using Indices and Artificial Neural Networks (ANNs) for the basin. Hydro-meteorlogical data for the period 1970-2010 at sixteen hydrometric stations was used to test the performance of the indices in forecasting of the future drought at 1, 3, 6, 9, 12, 18 and 24-months lead times, by constructing ANN models with different time delays. Drought conditions at monthly temporal resolution were evaluated using selected drought indices. The occurrence of drought was investigated using non-parametric Man-kendall trend test. Spatial distribution of drought severity was determined using Kriging interpolation techinique. In addition, a standard Nonlinear-Integrated Drought Index (NDI), for drought forecasting in the basin was developed using hydro-meteoroogical data for the river basin. The results of spaial drought show that the south-eastern parts of the basin are more prone to drought risks than the north-western areas. The Mann-Kendall trend test indicates an increasing drought trend in the south-eastern and no trend in north-western areas of the basin. Development of Surface Water Supply Index (SWSI) function, NDI and characteristic curves defining the return period and the probability of different drought magnitudes based on Drought Indices (DIs) was achieved. Drought Severity-Duration-Frequency (SDF) curves were developed. The formulated NDI tool can be adopted for a synchronized assessment and forecasting of all the three operational drought types in the basin. The results can be used in assisting water resources managers for timely detection and forecasting of drought conditions in prioritized planning of drought preparedness and early warning systems.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya (2017)
ISBN: 9783668917484 bzw. 3668917485, vermutlich in Englisch, GRIN Verlag, Taschenbuch, neu, Nachdruck.
PRINT ON DEMAND Book; New; Publication Year 2017; Not Signed; Fast Shipping from the UK.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya
ISBN: 9783668917477 bzw. 3668917477, vermutlich in Englisch, GRIN Verlag, Taschenbuch, neu.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana River Basin, Kenya
ISBN: 9783668917477 bzw. 3668917477, vermutlich in Englisch, neu, E-Book, elektronischer Download.
Spatio-Temporal Drought Characterization and Forecasting Using Indices and Artificial Neural Networks. A Case of the Upper Tana Ri
ISBN: 9783668917484 bzw. 3668917485, vermutlich in Englisch, Bod Third Party Titles, Taschenbuch, neu.
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