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Benito, G.; Rohde, R.; Seely, M.; Külls, C.; Dahan, O.; Enzel, Y.; Todd, S.; Botero, B.; Morin, E.; Grodek, T. |
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Management of alluvial aquifers in two southern African ephemeral rivers: implications for IWRM |
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2010 |
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Water Resources Management |
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24 |
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4 |
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641-667 |
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Springer Netherlands |
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THL @ christoph.kuells @ Benito2010management |
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25 |
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Author |
Karaimeh, S.A. |
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Title |
Maintaining desert cultivation: Roman, Byzantine, and Early Islamic water-strategies at Udhruh region, Jordan |
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Journal Article |
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Year |
2019 |
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Journal of Arid Environments |
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166 |
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108-115 |
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Irrigation, Qanat, Cultivation, Arid environment, Nabataean, Jordan |
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Abstract |
The site of Udhruh is located in the arid desert of southern Jordan, about 15 km to the east of Petra. The site was built by the Nabataeans but expanded by the Romans (as a defensive site) and was continuously occupied until the Early Islamic period. It receives less than the 200 mm of annual precipitation, which is crucial for agricultural cultivation. Archaeological evidence from earlier excavations together with new data from several survey projects indicate that areas around Udhruh were cultivated throughout the Roman, Byzantine, and Early Islamic periods (300 BCE–800 CE). The fundamental question is: how did the people of Udhruh sustain their community in the desert, and how did they transform the desert into arable land? The landscape could be utilised thanks to sophisticated water management and irrigation techniques. At least four underground qanat systems were identified providing Udhruh with access to groundwater. At the terminal end of the qanat systems, several types of closed surface channels conveyed the water to reservoirs, which subsequently distributed the water to the field systems. The water systems of Udhruh differ from the well-known Nabataean systems in the surrounding area. As Udhruh was taken over by the Roman army in 106 CE, this study analyses how the Nabataean water systems continued to function and adapt through the Roman and Byzantine periods. A complete understanding of Udhruh’s water systems helps to reconstruct past land use, agricultural activity, and irrigation practices in a currently arid region. |
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0140-1963 |
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THL @ christoph.kuells @ Alkaraimeh2019108 |
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271 |
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Klaus, J.; Zehe, E.; Elsner, M.; Külls, C.; McDonnell, J.J. |
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Title |
Macropore flow of old water revisited: experimental insights from a tile-drained hillslope |
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2013 |
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Hydrology and Earth System Sciences |
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17 |
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1 |
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103 |
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Copernicus GmbH |
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THL @ christoph.kuells @ Klaus2013macropore |
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23 |
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Wang, B.; Luo, Y.; Qian, J.-zhong; Liu, J.-hui; Li, X.; Zhang, Y.-hong; Chen, Q.-qian; Li, L.-yao; Liang, D.-ye; Huang, J. |
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Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium |
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Journal Article |
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2023 |
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Journal of Hydrology |
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626 |
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130234 |
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In-situ leaching, Injection rate design, Lixiviant concentration design, Machine learning, Simulation-optimisation, Uncertainty |
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Abstract |
The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights. |
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0022-1694 |
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THL @ christoph.kuells @ wang_machine_2023 |
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210 |
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Author |
Merembayev, T.; Yunussov, R.; Yedilkhan, A. |
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Title |
Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits |
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Journal Article |
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2019 |
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Procedia Computer Science |
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150 |
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46-52 |
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classification, geophysics logging data, machine learning, stratigraphy, uranium deposit |
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Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words – it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost. |
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1877-0509 |
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THL @ christoph.kuells @ merembayev_machine_2019 |
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113 |
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