|   | 
Details
   web
Records
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.
Address
Corporate Author Thesis
Publisher Place of Publication 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 (down) Approved no
Call Number THL @ christoph.kuells @ Aderemi2023200049 Serial 219
Permanent link to this record
 

 
Author Konapala, G.; Mishra, A.K.; Wada, Y.; Mann, M.E.
Title Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation Type Journal Article
Year 2020 Publication Nature Communications Abbreviated Journal
Volume 11 Issue 1 Pages 3044
Keywords
Abstract Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems. Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint. Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality. These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections. Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation. Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of “seasonally variable regimes becoming more variable”. Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2041-1723 ISBN Medium
Area Expedition Conference
Notes (down) Approved no
Call Number THL @ christoph.kuells @ Konapala2020 Serial 284
Permanent link to this record
 

 
Author Wolfe, P.
Title The Simplex Method For Quadratic Programming Type Journal Article
Year 1959 Publication Econometrica Abbreviated Journal
Volume 27 Issue Pages 170
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication 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 (down) Approved no
Call Number THL @ christoph.kuells @ Wolfe1959 Serial 285
Permanent link to this record
 

 
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.
Address
Corporate Author Thesis
Publisher Place of Publication 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 (down) Approved no
Call Number THL @ christoph.kuells @ Ollivier2023101400 Serial 288
Permanent link to this record
 

 
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
Address
Corporate Author Thesis
Publisher NERC EDS Environmental Information Data Centre Place of Publication 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 (down) Approved no
Call Number THL @ christoph.kuells @ ref10.5285/f93d87ed-7bc4-4d03-9690-3856e6cbbd11 Serial 289
Permanent link to this record