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Ardelt, G.; Külls, C.; Hellbrück, H. |
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Title |
Towards intrinsic molecular communication using isotopic isomerism |
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Journal Article |
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2018 |
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Open Journal of Internet Of Things (OJIOT) |
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4 |
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1 |
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135-143 |
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RonPub |
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THL @ christoph.kuells @ Ardelt2018towards |
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18 |
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Benites Lazaro, L.L.; Bellezoni, R.; Puppim de Oliveira, J.; Jacobi, P.R.; Giatti, L. |
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Title |
Ten Years of Research on the Water-Energy-Food Nexus: An Analysis of Topics Evolution |
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Journal Article |
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Year |
2022 |
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Frontiers in Water |
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4 |
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THL @ christoph.kuells @ article |
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86 |
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Hamidian, A.; Ghorbani, M.; Abdolshahnejad, M.; Abdolshahnejad, A. |
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Title |
RETRACTED: Qanat, Traditional Eco-technology for Irrigation and Water Management |
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Journal Article |
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Year |
2015 |
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Agriculture and Agricultural Science Procedia |
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4 |
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119-125 |
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This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of Editor.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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2210-7843 |
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Efficient irrigation management and its effects in urban and rural landscapes |
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no |
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THL @ christoph.kuells @ Hamidian2015119 |
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252 |
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Joseph, J.; Külls, C.; Arend, M.; Schaub, M.; Hagedorn, F.; Gessler, A.; Weiler, M. |
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Application of a laser-based spectrometer for continuous in situ measurements of stable isotopes of soil CO2 in calcareous and acidic soils |
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Journal Article |
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2019 |
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Soil |
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5 |
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1 |
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49-62 |
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Copernicus |
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THL @ christoph.kuells @ Joseph2019application |
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15 |
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Author |
Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Title |
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 |
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Year |
2023 |
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Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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