Knowledge Discovery From Satellite Images For Drought Monitoring
| dc.contributor.advisor | Alnafu, Solomon(PhD) | |
| dc.contributor.author | Beman, Getachew | |
| dc.date.accessioned | 2021-12-13T06:25:32Z | |
| dc.date.accessioned | 2023-11-18T09:51:16Z | |
| dc.date.available | 2021-12-13T06:25:32Z | |
| dc.date.available | 2023-11-18T09:51:16Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Drought is one of the most impo rtant challenges facing the planet. When it happens, it usually re sults in serious econom ic. environmental, and social cr ises. Despite the growi ng number of freely available biophysical, climate. and satellite data for characterizing and modeli ng drought, research efforts have been constrained to using only meteo rological point data, such as the amount of rainfall, for drought monitoring information. This po int data is insufficient for representing diversified ecosystems, and the data has coarse reso lut ion levels (lim ited spatial coverage). Researchers also have limited tools for data retrieval and integration for improved drought identification and model ing. which usua lly results in a time de lay fo r informat ion to reach dec is ion makers. Taking this into account, this dissertation researc h has three objectives: I) identify the most re levant attr ibutes for effic ient ly implementing drought monitoring, 2) develop a new approach for extracting knowledge from sate ll ite imageries for improved ident ifica tion and pred iction of drought, and 3) evaluate the new approach for national and regio nal dro ught prediction appl ications. Using an exploratory research approach and modeli ng research method, different data co llect ion and analys is techniques were executed using knowledge d iscovery in a database approach. The data mi ning models developed using art ificial neural network and regress ion tree models were able to predict DroughtObject with accuracy of 0.70 - 0.95 co rrelat ion coefficients. in a neta four month s' time lag. The develo ped DroughtObject model was evaluated for its application in showing drought severity and food defic it status. There were positive relat ionships between DroughtObjecl products and crop yield data up to 0.91 R2 values. The results confirmed that the model can direct ly be used by those who are currently responsible for drought monitoring and ri sk management. The new concept developed in this research was prolotypcd and demonstrated in an easy-tousc approach. with a focus on demonstfaling the concept of DroughtObjecl characterization and identification fro m a group of pixels. This demonSiration also revealed poss ible future system deve lopments. This di ssert ation research could he lp deci sion makers use advanced satellite technology fo r crrcctive drought monitoring and early warning systems in va rious regio ns. Combined with proper pol ic ies. Ihese systems can he lp to prevent famine and starvat ion in food-insecure reg ions. Up to now, satellite technologies have been used primarily in areas of meteoro logical applications. In this research. the main emphas is is on mining knowledge from satell ite images for dro ught ri sk assessment and sa ving the lives of individua ls who are affected by recurring drought s. The findings of this research can help decision makers take time ly and appropriate actions to save lives in drought-affected areas using advanced satellite techno logy. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/29217 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa | en_US |
| dc.subject | Drought Monitoring; Drought Prediction; Geo-spat ial Informat ion; Knowledge Discovery; Satell ite Image | en_US |
| dc.title | Knowledge Discovery From Satellite Images For Drought Monitoring | en_US |
| dc.type | Thesis | en_US |