| Records |
| Author |
Alexander, A.C.; Ndambuki, J.M. |
| Title |
Impact of mine closure on groundwater resource: Experience from Westrand Basin-South Africa |
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Journal Article |
| Year |
2023 |
Publication |
Physics and Chemistry of the Earth, Parts A/B/C |
Abbreviated Journal |
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| Volume |
131 |
Issue |
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Pages |
103432 |
| Keywords |
Acid mine drainage, Groundwater quality, Mine closure, Spatio-temporal variation, Westrand Basin |
| Abstract |
The mining sector is at the edge of expanding to cater for natural resources that are much needed for technological development and manufacturing. Mushrooming of mines will consequently increase the number of mines closure. Moreover, mines closure have adverse impact on the environment at large and specifically on water resources. This study analyses historical groundwater quality parameters in mine intensive basin of Westrand Basin (WRB) to understand the status of groundwater quality in relation to mining activities and mine closure. Geographic information system (GIS) was used to map the spatio-temporal variation of groundwater quality in the basin and groundwater quality index (GQI) to evaluate its status. The coefficient of variation (CV) was applied to understand the stability of groundwater quality after the mine closure. Results indicated unstable and altered trend with increasing levels of acidity and salts concentration around the mines vicinity following the mine closure. The resultant maps indicated a significant deterioration of groundwater quality around the WRB with concentrations decreasing downstream. Obtained average GQI for the study period of 1996–2015 suggested a moderate groundwater quality at a range of GQI = 64–73. The CV indicated varying water quality at CV \textgreater 30% suggesting presence of source of contamination. Observed groundwater quality trends in Westrand basin suggested that mines closure present potential threat on groundwater quality and thus, a need for a robust mine closure plan and implementation. |
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1474-7065 |
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THL @ christoph.kuells @ alexander_impact_2023 |
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134 |
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Akter, A.; Tanim, A.H.; Islam, M.K. |
| Title |
Possibilities of urban flood reduction through distributed-scale rainwater harvesting |
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Journal Article |
| Year |
2020 |
Publication |
Water Science and Engineering |
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13 |
Issue |
2 |
Pages |
95-105 |
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Low-impact development (LID), SWMM, HEC-RAS, Remote sensing, Urban flooding, Inundation depth |
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Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting (RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model (SWMM) was employed with rain barrel low-impact development (LID) as a flood reduction measure. The Hydrologic Engineering Center’s River Analysis System (HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction. Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match, i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%–60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04–0.45 m3 from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area. |
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1674-2370 |
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THL @ christoph.kuells @ Akter202095 |
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247 |
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Adolph, G.; Römer, T.; Külls, C. |
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Deriving complex groundwater age structure by combining age dating and analytic element modelling |
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Conference Article |
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G-DAT 2008-Leipzig |
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12 |
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THL @ christoph.kuells @ Adolph2008deriving |
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55 |
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Adolph, G.; KÜlls, C.; Willscheid, A. |
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Determination and validation of age structures as an improved measure of hydrological dynamics |
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2007 |
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Geophysical Research Abstracts |
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9 |
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08013 |
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THL @ christoph.kuells @ Adolph2007determination |
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58 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
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Journal Article |
| Year |
2023 |
Publication |
Systems and Soft Computing |
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5 |
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Pages |
200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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