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Author Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S.
Title Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa Type Journal Article
Year 2023 Publication Systems and Soft Computing Abbreviated Journal
Volume 5 Issue Pages 200049
Keywords Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series
Abstract The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.
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Corporate Author Thesis
Publisher Place of Publication (up) Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2772-9419 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number THL @ christoph.kuells @ Aderemi2023200049 Serial 219
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Author Ollivier, C.C.; Carrière, S.D.; Heath, T.; Olioso, A.; Rabefitia, Z.; Rakoto, H.; Oudin, L.; Satgé, F.
Title Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar Type Journal Article
Year 2023 Publication Journal of Hydrology: Regional Studies Abbreviated Journal
Volume 47 Issue Pages 101400
Keywords Precipitation products, Remote sensing, Ensemble approach, Hydrology, Madagascar
Abstract Study region this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focus the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity.
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Corporate Author Thesis
Publisher Place of Publication (up) Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2214-5818 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number THL @ christoph.kuells @ Ollivier2023101400 Serial 288
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Author Zwartendijk, B.W.; Ghimire C. P.; Ravelona M.; Lahitiana J.; van Meerveld H. J.
Title Hydrometric data and stable isotope data for streamflow and rainfall in the Marolaona catchment, Madagascar, 2015-2016 Type Miscellaneous
Year 2023 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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Corporate Author Thesis
Publisher NERC EDS Environmental Information Data Centre Place of Publication (up) Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number THL @ christoph.kuells @ ref10.5285/f93d87ed-7bc4-4d03-9690-3856e6cbbd11 Serial 289
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Author Mekuria, W.; Tegegne, D.
Title Water harvesting Type Book Chapter
Year 2023 Publication Encyclopedia of Soils in the Environment (Second Edition) Abbreviated Journal
Volume Issue Pages 593-607
Keywords Climate change, Ecosystem services, Environmental benefits, Population growth, Resilient community, Resilient environment, Socio-economic benefits, Urbanizations, Water harvesting, Water quality, Water security
Abstract Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques.
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Corporate Author Thesis
Publisher Academic Press Place of Publication (up) Oxford Editor Goss, M.J.; Oliver, M.
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-323-95133-3 Medium
Area Expedition Conference
Notes Approved no
Call Number THL @ christoph.kuells @ Mekuria2023593 Serial 225
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Author Mekuria, W.; Tegegne, D.
Title Water harvesting Type Book Chapter
Year 2023 Publication Encyclopedia of Soils in the Environment (Second Edition) Abbreviated Journal
Volume Issue Pages 593-607
Keywords Climate change, Ecosystem services, Environmental benefits, Population growth, Resilient community, Resilient environment, Socio-economic benefits, Urbanizations, Water harvesting, Water quality, Water security
Abstract Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques.
Address
Corporate Author Thesis
Publisher Academic Press Place of Publication (up) Oxford Editor Goss, M.J.; Oliver, M.
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-323-95133-3 Medium
Area Expedition Conference
Notes Approved no
Call Number THL @ christoph.kuells @ Mekuria2023593 Serial 265
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