A new modelling framework to assess changes in groundwater level
نویسندگان
چکیده
The Southern African region consists of ten countries including, Angola, Zambia, Malawi, Namibia, Botswana, Zimbabwe, Mozambique, South Africa, Lesotho, and Swaziland. Frequent drought episodes in the arid semi-arid parts suggest impact climate change need for a sustainable framework groundwater level assessments. In this study, we developed machine learning modelling based on deep belief network (DBN) to predict changes monthly levels (CGWLs) at 1–5 month time scales 27 wells over southern Africa region. With predictor dataset constituted by hydrological parameters, estimates, global indices, ascertain possibility forecasting (CGWL) up 5-month lead times most locations study Using quantile regression technique, strength DBN 90% 95 % confidence were tested, helped determine accuracy our forecasts five months four representative wells. Deep offers new capabilities evaluating non-linear systems, including analysis. re-injection procedure network, which allows prior CGWL estimates serve as inputs next estimate was key maintaining forecast 4th 5th times. This evidenced (r = 0.91, 0.87, 0.81, 0.75), formed part test samples We also observed that is highly susceptible local variables are important drivers change, can have strong fluctuations. Therefore, proved be robust algorithm general assessments
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ژورنال
عنوان ژورنال: Journal of Hydrology: Regional Studies
سال: 2022
ISSN: ['2214-5818']
DOI: https://doi.org/10.1016/j.ejrh.2022.101185