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Author (up) Abadi, B.; Sadeghfam, S.; Ehsanitabar, A.; Nadiri, A.A. url  openurl
  Title Investigating socio-economic and hydrological sustainability of ancient Qanat water systems in arid regions of central Iran Type Journal Article
  Year 2023 Publication Groundwater for Sustainable Development Abbreviated Journal  
  Volume 23 Issue Pages 100988  
  Keywords Ancient irrigation, QWSs, GIS, Indigenous knowledge, Maintenance, Distribution  
  Abstract The Qanat water systems (QWSs), the ancient water engineering systems in Iran belonging to the very distant past, have harvested groundwater from drainages to convey it toward the surface with no use of energy. The present article highlights the socio-economic aspects of the sustainability of the QWSs and gives a satisfactory explanation of why the QWSs should be restored. In doing so, we subscribe to the view that indigenous and scientific knowledge should be incorporated. The former serves to tackle the restoration of the QWSs, the latter contributes to the distribution of water into the farmlands as efficiently as possible. Measured by (a) resilience, (b) reliability, (c) vulnerability, and (d) sustainability, the GIS technique made clear the performance of the QWSs has, therefore, the worst condition observed in terms of resiliency; the best condition observed concerning the vulnerability. Moreover, the QWSs have intermediate performance in terms of reliability. Finally, the sustainability index (SI) classifies the QWSs into different bands, which provide explicit support to take priority of the selection of the QWSs for restoration. In conclusion, a theoretical framework has been drawn to keep the QWSs sustainable.  
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  ISSN 2352-801x ISBN Medium  
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  Call Number THL @ christoph.kuells @ Abadi2023100988 Serial 268  
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Author (up) Abiye, T. url  openurl
  Title Synthesis on groundwater recharge in Southern Africa: A supporting tool for groundwater users Type Journal Article
  Year 2016 Publication Groundwater for Sustainable Development Abbreviated Journal  
  Volume 2-3 Issue Pages 182-189  
  Keywords Arid and semi-arid areas, Groundwater recharge, Recharge estimation methods, Southern Africa  
  Abstract This synthesis on groundwater recharge targets the Southern African region as a result of the dependence of the community and economic sector on the groundwater resource. Several literature based recharge studies were collected and assessed in order to find out the main controls to the occurrence of recharge. The Water Table Fluctuation and Base flow separation methods have been tested in the catchment that drains crystalline basement rocks and dolostones close to the city of Johannesburg, South Africa. Based on the assessed data the Chloride Mass Balance method resulted in groundwater recharge of less than 4% of the rainfall, while it reaches 20%, when rainfall exceeds 600mm. For the classical water balance method, recharge proportion is less than 3% of rainfall as a result of very high ambient temperature in the region. Based on the Saturated Volume Fluctuation and Water Table Fluctuation methods, recharge could be less than 6% for annual rainfall of less than 600mm. Observational results further suggest that sporadic recharge from high intensity rainfall has important contribution to the groundwater recharge in the region, owing to the presence of permeable geological cover, which could not be fully captured by most of the recharge estimation methods. This study further documents an evaluation of the most reliable recharge estimation methods in the area such as the chloride mass balance, saturated volume fluctuation and water table fluctuation methods in order to successfully manage the groundwater resource.  
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  Call Number THL @ christoph.kuells @ abiye_synthesis_2016 Serial 101  
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Author (up) Adar, E.M.; Külls, C. openurl 
  Title MCM sf–Mixing-cell model for a steady flow MIG–Mixing-cell input generator: A short manual for installation and operation of MCM sf using the MIG–mixing-cell input generator Type Report
  Year 2002 Publication Abbreviated Journal  
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  Call Number THL @ christoph.kuells @ adar2002mcm Serial 67  
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Author (up) Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. url  openurl
  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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  Call Number THL @ christoph.kuells @ Aderemi2023200049 Serial 219  
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Author (up) Adolph, G.; KÜlls, C.; Willscheid, A. openurl 
  Title Determination and validation of age structures as an improved measure of hydrological dynamics Type Conference Article
  Year 2007 Publication Geophysical Research Abstracts Abbreviated Journal  
  Volume 9 Issue 08013 Pages  
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  Call Number THL @ christoph.kuells @ Adolph2007determination Serial 58  
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