Advances In Decision Support Systems by Ger Devlin

By Ger Devlin

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3, pp. ; Pancaldi, D. & Alberti, I. (1996). Appearance of Puccinia recondita f sp tritici on winter wheat: a simulation model. EPPO Bulletin, Vol. 26, No. 3-4, (September 1996) pp. ; Pancaldi, D. & Alberti, I. (1997). A simulation model for the development of brown rust epidemics in winter wheat. European Journal of Plant Pathology, Vol. 103, No. 5, (July 1997) pp. ; Ponti, I. & Cravedi, P. (2000). The status of warning services for plant pests in Italy. EPPO Bulletin, Vol. 30, No. 1, (March 2000) pp.

Sherman, G. (2008). Desktop GIS: Mapping the Planet with Open Source Tools. , ISBN-13: 978-1-934356-06-7, USA Sparkes, A. & Kindner, D. (1996). A GIS for the Environmental Impact Assessment of Wind Farms, ESRI Stagl, S. (2006). Multicriteria evaluation and public participation: the case of UK energy policy. A. (1965). Fuzzy Sets. M. & Fabbri A. G. (2008). Application of spatial multi-criteria analysis to site selection for local park: A case study in the Bergamo Province, Italy. Journal of Environmental Management, 88, 752-769 Development of an Open Source GIS Based Decision Support System for Locating Wind Farms in Wallonia (Southern Belgium) 41 Appendix 1a – list of the selected ‘environmental’ constraint criteria Category ID 1 2 3 4 5 6 7 8 9 Environmental 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Criteria Alias Special Protection SPA Area Natura 2000 sites N2000 Site of ornithological BIRDS value Nature Reserve RES Underground cavity CSIS of scientific interest Central zones of Local CZLPND Plan for Nature Development zhib ZHIB Site of high biological SHBV value Airports AIRP Airplane landing ALR runway Microlight landing MLR runway Acoustic effect ACOU Zone of potential FLICK flicker effect High voltage lines HVL Proximity to roads ROAD Proximity to railroad RAIL “Green” parks GREEN Area devoted to NAT nature Parks PARK Close protection of CPWPZ water producing zone Remote protection of RPWPZ water producing zone Landslide risk LSL Area with important IKR karstic risks Area with karstic risks KR Risk of interference ANT with radio antenna Reference Constra Geo features int level processing Buffer zone (m) Polygon EX feature Polygon SE feature Polygon HS buffer Polygon EX feature Point EX buffer Polygon EX feature Polygon EX feature Polygon SE feature Polygon HS buffer Point HS feature Point SE buffer 2000 Polygon EX buffer 350 Polygon SE buffer 700 Line Line Line Polygon HS EX EX EX buffer buffer buffer feature 150 40 40 Polygon EX buffer 200 Polygon EX feature Polygon EX feature Polygon SE feature Polygon EX feature Polygon EX feature Polygon SE feature Polygon HS buffer 150 100 5000 600 42 Decision Support Systems, Advances in Appendix 1b – list of the selected ‘landscape’ constraint criteria Category ID 26 27 28 29 Alias Heritage landscapes Highly sensitive landscapes & territories Small wood lots PL Polygon EX feature HSLT Polygon HS feature SWL Polygon HS feature Buffer zone (m) Hilly forests Forest with recreational value Nature parks HF Polygon HS feature FRV Polygon HS feature NP Polygon SE feature 32 Large common rural landscapes LCRL Polygon HS feature 33 Proto-industrial landscape units PILU Polygon EX feature 34 Industrial landscape units with heritage value ILPV Point SE buffer 35 Urban landscape units with heritage value ULPV Point EX feature 36 Rural landscape units with heritage value RLPV Polygon SE buffer 2000 37 Linear features with heritage value LFPV Line HS buffer 1250 38 Viewshed of classified monuments VCM Polygon HS feature ZLV Polygon SE feature PLV Polygon SE feature 30 31 Landscape Reference Constra Geo features int level processing Criteria 39 40 Zones of landscape value Perimeter of landscape value 2000 3 Forecasting Rubber Production using Intelligent Time Series Analysis to Support Decision Makers Panida Subsorn, Dr.

Basically, they focus on improvement to gain better performance or to deal with current and future problems. However, some forecasting techniques may not manage particular situations or problems efficiently as they may not be entirely accurate. Hence, this study attempts to gain a feasible forecasting model based on a comparative method and to select a forecasting model which supplies less forecasting errors for this selected sector. In order to provide a better understanding, the following subsections provide a description of non-neural network and neural network Techniques, three main components and data analysis procedures for a feasible new forecasting model for the public agricultural rubber industry in Thailand.

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