气候变化领域集成服务门户(CCPortal): Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land-Water Nexus

CCPortal

DOI	10.3390/su16041699
	Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land-Water Nexus
	Ouma, Yashon O.; Nkwae, Boipuso; Odirile, Phillimon; Moalafhi, Ditiro B.; Anderson, George; Parida, Bhagabat; Qi, Jiaguo
发表日期	2024
EISSN	2071-1050
起始页码	16
结束页码	4
卷号	16 期号:4
英文摘要	For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression-cellular automata (LR-CA) and artificial neural network-cellular automata (ANN-CA), for the prediction of LULCC (2019-2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986-2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (-8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (-2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land-water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.
英文关键词	land-use land-cover (LULC) change; logistic regression; artificial neural network; cellular automata; random forest regression; sustainable land-water nexus
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:001172347400001
来源期刊	SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300295
作者单位	University of Botswana; University of Botswana; Michigan State University
推荐引用方式 GB/T 7714	Ouma, Yashon O.,Nkwae, Boipuso,Odirile, Phillimon,et al. Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land-Water Nexus[J],2024,16(4).
APA	Ouma, Yashon O..,Nkwae, Boipuso.,Odirile, Phillimon.,Moalafhi, Ditiro B..,Anderson, George.,...&Qi, Jiaguo.(2024).Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land-Water Nexus.SUSTAINABILITY,16(4).
MLA	Ouma, Yashon O.,et al."Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land-Water Nexus".SUSTAINABILITY 16.4(2024).