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Benites Lazaro, L. L., Bellezoni, R., Puppim de Oliveira, J., Jacobi, P. R., & Giatti, L. (2022). Ten Years of Research on the Water-Energy-Food Nexus: An Analysis of Topics Evolution. Frontiers in Water, 4.
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Hamidian, A., Ghorbani, M., Abdolshahnejad, M., & Abdolshahnejad, A. (2015). RETRACTED: Qanat, Traditional Eco-technology for Irrigation and Water Management. Agriculture and Agricultural Science Procedia, 4, 119–125.
Abstract: This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of Editor. The authors have plagiarized part of a book Veins of Desert, by Semsar Yazdi, Ali Asghar; Labbaf Khaneiki, Majid published by UNESCO-ICQHS, 2010 pages 2, 3, 5, 6, 7, 11, 44, 156, 157 and 158. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited.
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Joseph, J., Külls, C., Arend, M., Schaub, M., Hagedorn, F., Gessler, A., et al. (2019). Application of a laser-based spectrometer for continuous in situ measurements of stable isotopes of soil CO2 in calcareous and acidic soils. Soil, 5(1), 49–62.
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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
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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Vushe, A., & Amutenya, M. (2019). Investigating nitrate retention capacity, elementary and mineral composition of Kalahari sandy soils at Mashare farm in Namibia, Okavango river basin. Scientific African, 6, 00193.
Abstract: Kalahari sands which cover a large part of Southern Africa and extend into Central Africa are infertile and marginal soils for intensive agriculture. Therefore, high nitrogen fertilisation rates may degrade ecosystems of rivers with catchments covered by the Kalahari sands. A study on Mashare Farm located in the Okavango River basin showed that irrigated Kalahari sandy soils had a nitrate retention capacity, which enabled the soil to resist nitrate leaching in water saturated conditions. The irrigated soils were modified by agricultural activities; hence this study investigated if uncultivated and cultivated Kalahari sand soils had similar nitrate retention properties. The elementary composition of the soils was investigated for obtaining an insight into chemical properties that may be causing the nitrate retention capacity. A permeameter was used to leach out nitrates from irrigated and uncultivated soil samples, and nitrate concentrations were measured on the leaching effluent from the permeameter. Elemental analysis was done on the cultivated and the uncultivated soil samples using a Scanning Electron Microscope, a portable X-Ray Fluorescence analyzer, and an X-Ray Diffraction machine, and the later was also used for crystalline structure analyses. Sieve analyses confirmed that the Mashare’s cultivated and uncultivated topsoils were similar, and both were similar to Botswana Kalahari topsoil. The irrigated and cultivated subsoil had a higher average nitrate retention capacity of 76% compared to 73% for the uncultivated subsoil. Both samples had the same elements, although the proportions were different. Both soil samples were dominated by a quartz mineral, but the field soil had traces of palygorskite. The presence of aluminum and transition metals outside the minerals structure, but as coatings on the quartz sand grains enhanced nitrate retention capacity properties of the Kalahari sand soils.
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