Your search found 55 records
1 Walker, S.; Tsubo, M. 2003. Predicting rainfall intensity from daily rainfall data. In Beukes, D.; de Villiers, M.; Mkhize, S.; Sally, H.; van Rensburg, L. (Eds.). Proceedings of the Symposium and Workshop on Water Conservation Technologies for Sustainable Dryland Agriculture in Sub-Saharan Africa (WCT), held at Bloem Spa Lodge and Conference Centre, Bloemfontein, South Africa, 8-11 April 2003. Pretoria, South Africa: ARC-Institute for Soil, Climate and Water. pp.134-141.
(Location: IWMI-HQ Call no: IWMI 631.7.1 G100 BEU Record No: H034397)
2 Cancelliere, A.; Giuliano, G.; Ancarani, A.; Rossi, G. 2003. Derivation of operation rules for an irrigation water supply system by multiple linear regression and neural networks. In Rossi, G.; Cancelliere, A.; Pereira, L. S.; Oweis, T.; Shatanawi, M.; Zairi, A. (Eds.), Tools for drought mitigation in Mediterranean regions. Dordrecht, Netherlands: Kluwer. pp.275-291.
(Location: IWMI-HQ Call no: 338.14 GG20 ROS Record No: H036880)
3 Trajkovic, S. 2005. Temperature-based approaches for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 131(4):316-323.
(Location: IWMI-HQ Call no: PER Record No: H037980)
4 Sarangi, A.; Bhattacharya, A. K. 2005. Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management, 78(3):195-208.
Hydrology ; Models ; Neural networks ; Forecasting ; Rainfall-runoff relationships ; Watersheds ; Sedimentation / India / Banha Watershed
(Location: IWMI-HQ Call no: PER Record No: H038069)
5 Kingston, G. B.; Maier,H. R.; Lambert, M. F. 2005. Calibration and validation of neural networks to ensure physically plausible hydrological modeling. Journal of Hydrology, 314: 158–176.
(Location: IWMI-HQ Call no: P 7448 Record No: H037930)
6 Ahmad, S.; Simonovic, S. P. 2005. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology, 315:236-251.
Hydrology ; Runoff ; River basins ; Watersheds ; Precipitation ; Forecasting ; Models ; Neural networks ; Performance evaluation / Canada / Manitoba / Red River
(Location: IWMI-HQ Call no: P 7455 Record No: H037944)
7 Indian Society of Soil Science. 2006. International Conference on Soil, Water and Environmental Quality: Issues and Strategies, Proceedings, New Delhi, India, 28 January – 1 February 2005. New Delhi, India: Indian Society of Soil Science. 484p.
Water management ; Land management ; Soil management ; Environmental effects ; Watershed management ; Water quality ; Water reuse ; Irrigated farming ; Rice ; Wheat ; Neural networks ; Agroforestry ; Farming systems ; Climate change ; Remote sensing / Asia / India / Ethiopia
(Location: IWMI-HQ Call no: 333.91 G635 IND Record No: H038923)
8 Singh, A. K. 2006. Use of artificial neural networks in soil science. In Indian Society of Soil Science. International Conference on Soil, Water and Environmental Quality: Issues and Strategies, Proceedings, New Delhi, India, 28 January – 1 February 2005. New Delhi, India: Indian Society of Soil Science. pp.97-109.
Simulation models ; Neural networks ; Soil properties ; Soil water ; Subsurface drainage ; Drip irrigation
(Location: IWMI-HQ Call no: 333.91 G635 IND Record No: H038929)
9 Jianwen, M.; Bagan, H. 2005. Land-use classification using ASTER data and self-organized neural networks. International Journal of Applied Earth Observation and Geoinformation, 7(3):183-188.
(Location: IWMI-HQ Call no: P 7664 Record No: H039413)
10 Solomatine, D. P.; Yunpeng, X.; Chuanbao, Z.; Yan, L. 2003. Application of data-driven modelling to flood forecasting with a case study for the Huai River in China. In Yellow River Conservancy Commission. Proceedings, 1st International Yellow River Forum on River Basin Management – Volume III. Zhengzhou, China: The Yellow River Conservancy Publishing House. pp.140-150.
(Location: IWMI-HQ Call no: 333.91 G592 YEL Record No: H034669)
11 Prasad, R. K.; Mathur, S. 2007. Groundwater flow and contaminant transport simulation with imprecise parameters. Journal of Irrigation and Drainage Engineering, 133(1):61-70.
(Location: IWMI HQ Call no: PER Record No: H040018)
12 Mazvimavi, D.; Meijerink, A. M. J.; Savenije, H H. G.; Stein, A. 2005. Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe. Physics and Chemistry of the Earth, 30:639-647.
(Location: IWMI HQ Call no: P 8020 Record No: H040025)
13 Holz, K. P.; Hildebrandt, G.; Weber, L. 2006. Concept for a web-based information system for flood management. Natural Hazards, 38:121-140.
Flood control ; Forecasting ; Models ; GIS ; Neural networks ; Computer techniques ; Information systems
(Location: IWMI HQ Call no: P 7928 Record No: H040259)
14 McAleer, M.; Jakeman, A. (Eds.) 1993. International Congress on Modelling and Simulation: Proceedings, Volume 1, The University of Western Australia, 6-10 December 1993. Perth, Australia: University of Western Australia. 454p.
Simulation models ; Sensitivity analysis ; Statistical methods ; Time series analysis ; Rainfall-runoff relationships ; Water balance ; Catchment areas ; Climate change ; Environmental degradation ; Ecology ; Stream flow ; Water quality ; Air pollution ; Neural networks ; Salinity / Australia / UK / USA / Russian Federation / China / Denmark / Brazil / Picaninny Creek / Wales / Plynlimon Catchments / Bass River / Queanbeyan River
(Location: IWMI HQ Call no: 003.3 G000 MCA Record No: H040378)
International Congress organised by Modelling and Simulation Society of Australia (MSSA), Inc., International Association for Mathematics and Computers in Simulation (IMACS), International Society for Ecological Modelling, and The International Environmetrics Society.
15 Ghassemi, F.; White, D.; Cuddy, S.; Nakanishi, T. (Eds.) 2001. MODSIM 2001, International Congress on Modelling and Simulation, The Australian National University, Canberra, Australia, 10-13 December 2001: Integrating Models for Natural Resources Management Across Disciplines, Issues and Scales: Proceedings, Volume 4, General Systems. Canberra, Australia: Modelling and Simulation Society of Australia and New Zealand. pp.1589-2178.
Simulation models ; Decision support tools ; GIS ; Natural resources management ; Reservoir operation ; Cyclones ; Storms ; Risk management ; Forest management ; Ecosystems ; Rice ; Nitrogen ; Wheat ; Dry farming ; Farming systems ; Water balance ; Erosion ; Livestock ; Agroforestry ; Drinking water ; Neural networks ; Rivers ; Water quality ; Soil salinity ; Groundwater / Australia / New Zealand
(Location: IWMI HQ Call no: 003.3 G000 GHA Record No: H040389)
16 Karamouz, M.; Tabari, M. M. R.; Kerachian, R. 2007. Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International, 32(1):163-176.
Surface water ; Groundwater ; Conjunctive use ; Irrigation water ; Neural networks ; Simulation models ; Optimization / Iran / Tehran Aquifer / Kan River
(Location: IWMI HQ Call no: P 7977 Record No: H040524)
17 Morid, S.; Smakhtin, Vladimir; Bagherzadeh, K. 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27:2103-2111.
Drought ; Forecasting ; Models ; Risk management ; Indicators ; Neural networks ; Time series / Iran / Tehran Province
(Location: IWMI HQ Call no: IWMI 551.5773 G690 MOR Record No: H040771)
18 Srinivasa Raju, K. (Ed.) 2006. Predictions in ungauged basins for sustainable water resources planning and management. New Delhi, India: Jain Brothers. 264p.
Water resource management ; Planning ; River basins ; Watersheds ; Models ; Hydrology ; Neural networks ; Flooding ; Estimation ; Forecasting ; GIS ; Remote sensing ; Mapping ; Rainfall-runoff relationships ; Rice / India
(Location: IWMI HQ Call no: 333.9162 G635 SRI Record No: H041733)
19 Chelmicki, W.; Siwek, J. (Eds.) 2009. Hydrological extremes in small basins: 12th Biennial International Conference of the Euromediterranean Network of Experimental and Representative Basins (ERB), Krakow, Poland, 18–20 September 2008: proceedings. Paris, France: UNESCO, IHP. 171p. (IHP-VII Technical Document in Hydrology 84 / UNESCO Working Series SC-2009/WS/11)
River basins ; Hydrology ; Surface water ; Groundwater ; Stream flow ; Monitoring ; Simulation models ; Rainfall-runoff relationships ; Neural networks ; Flooding ; Forecasting ; Dams ; Catchment areas ; GIS ; Watersheds ; Urbanization ; Salt water intrusion ; Water quality ; Runoff ; Soil moisture / UK / Poland / Austria / Spain / Portugal / Germany / Czech / Italy / Netherlands / Kenya / Nida River Basin / Goldbergkees Basin / Baltic Sea / Aixola River / Douro River / Lange Bramke / Sazava River / Ploucnice River / Nyando River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042254)
(28.51 MB)
20 UNESCO-IHP. 2010. Climate change and adaptation for water resources in Yellow River Basin, China: IHP VII technical document in hydrology. Beijing, China: UNESCO. 125p.
River basin management ; Water resource management ; Hydrology ; Runoff ; Climate change ; Neural networks ; Models ; Forecasting ; Reservoirs ; Sedimentation ; Stream flow / China / Yellow River / Qinhe River
(Location: IWMI HQ Call no: e-copy only Record No: H043157)
(34.70 MB)
This publication is compilation of research papers on impact of climate change and adaptation for water resources in Yellow River Basin under the MDG Achievement Fund supported UN China initiative of Climate Change Partnership Framework (CCPF). Some of the research papers were presented during the inception workshop and 4th Yellow River Forum. The editors changed the format of the papers for the sake of uniformity, from which we hope readers to feel comfortable, and rearranged the order of papers in order to reflect better where their contents are categorized into. However, only a minimum modification was made to the papers.
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