干旱区生态环境知识资源中心(Arid): A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring

Arid

DOI	10.3390/rs11091099
	A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring
	Adede, Chrisgone 1,2; Oboko, Robert 1; Wagacha, Peter Waiganjo 1; Atzberger, Clement 3
通讯作者	Adede, Chrisgone
来源期刊	REMOTE SENSING
ISSN	2072-4292
出版年	2019
卷号	11 期号:9
英文摘要	Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001-2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016-2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R-2 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R-2 of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%.
英文关键词	general additive model drought risk management early warning system model selection overfitting cross-validation
类型	Article
语种	英语
国家	Kenya ; Austria
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000469763600107
WOS关键词	RIVER-BASIN ; TIME-SERIES ; MODIS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218364
作者单位	1.Univ Nairobi UoN, Sch Comp & Informat, POB 30197, Nairobi 00100, Gpo, Kenya; 2.NDMA, Lonrho House Standard St,Box 53547, Nairobi 00200, Kenya; 3.Univ Nat Resources BOKU, Inst Surveying Remote Sensing & Land Informat, Peter Jordan Str 82, A-1190 Vienna, Austria
推荐引用方式 GB/T 7714	Adede, Chrisgone,Oboko, Robert,Wagacha, Peter Waiganjo,et al. A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring[J],2019,11(9).
APA	Adede, Chrisgone,Oboko, Robert,Wagacha, Peter Waiganjo,&Atzberger, Clement.(2019).A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring.REMOTE SENSING,11(9).
MLA	Adede, Chrisgone,et al."A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring".REMOTE SENSING 11.9(2019).