digitalearthafrica · mickwelli · May 7, 2025 · May 6, 2025
diff --git a/...cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/01_dam_volume_data_preprocessing.ipynb b/...cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/01_dam_volume_data_preprocessing.ipynb
@@ -28,9 +28,9 @@
    "source": [
     "\n",
     "## Description\n",
-    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling. \n",
+    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling [(Retief et al., 2025)](https://arxiv.org/abs/2502.19989). \n",
     "\n",
-    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype Digital Twin for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
+    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype [Digital Twin](https://digitaltwins.demos-only.iwmi.org/) for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
    ]
   },
   {
@@ -775,10 +775,16 @@
    "metadata": {},
    "source": [
     "**References:**\n",
-    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.\n",
-    "- Gurusinghe, T., Muthuwatta, L., Matheswaran, K., & Dickens, C. (2024). Developing a foundational hydrological model for the Limpopo River Basin using the Soil and Water Assessment Tool Plus (SWAT+). Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 14p.\n",
-    "- Leitão, P. C., Santos, F., Barreiros, D., Santos, H., Silva, P., Madushanka, T., Matheswaran, K., Mutuwatte, L., Vickneswaran, K., Retief, H., Dickens, C., Garcia Andarcia, M. (2024). Operational SWAT+ Model: Advancing Seasonal Forecasting in the Limpopo River Basin. Colombo, Sri Lanka:  International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation.\n",
-    "- Mallick, Archita & Ghosh, Surajit & De Sarkar, Kounik & Roy, Sudip. (2024). Reservoir Water Level Forecasting using Deep Learning Technique. Conference: 2024 IEEE India Geoscience and Remote Sensing Symposium"
+    "- Retief, H., Andarcia, M. G., Dickens, C., & Ghosh, S. (2025). Dam Volume Prediction Model Development Using ML Algorithms. arXiv preprint arXiv:2502.19989.\n",
+    "https://doi.org/10.48550/arXiv.2502.19989\n",
+    "\n",
+    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.https://hdl.handle.net/10568/151898\n",
+    "\n",
+    "- Chambel-Leitão, P.; Santos, F.; Barreiros, D.; Santos, H.; Silva, Paulo; Madushanka, Thilina; Matheswaran, Karthikeyan; Muthuwatta, Lal; Vickneswaran, Keerththanan; Retief, H.; Dickens, Chris; Garcia Andarcia, Mariangel. 2024. Operational SWAT+ model: advancing seasonal forecasting in the Limpopo River Basin. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 97p. https://hdl.handle.net/10568/155533\n",
+    "\n",
+    "- Maity, R., Srivastava, A., Sarkar, S. and Khan, M.I., 2024. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning. Applied Computing and Geosciences, 24, p.100206.https://doi.org/10.1016/j.acags.2024.100206\n",
+    "\n",
+    "- Pimenta, J., Fernandes, J.N. and Azevedo, A., 2025. Remote Sensing Tool for Reservoir Volume Estimation. Remote Sensing, 17(4), p.619.https://doi.org/10.3390/rs17040619"
    ]
   },
   {

diff --git a/Use_cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/02_dam_volume_model_training.ipynb b/Use_cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/02_dam_volume_model_training.ipynb
@@ -27,9 +27,9 @@
    "source": [
     "\n",
     "## Description\n",
-    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling. \n",
+    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling [(Retief et al., 2025)](https://arxiv.org/abs/2502.19989). \n",
     "\n",
-    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype Digital Twin for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
+    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype [Digital Twin](https://digitaltwins.demos-only.iwmi.org/) for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
    ]
   },
   {
@@ -312,10 +312,16 @@
    "metadata": {},
    "source": [
     "**References:**\n",
-    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.\n",
-    "- Gurusinghe, T., Muthuwatta, L., Matheswaran, K., & Dickens, C. (2024). Developing a foundational hydrological model for the Limpopo River Basin using the Soil and Water Assessment Tool Plus (SWAT+). Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 14p.\n",
-    "- Leitão, P. C., Santos, F., Barreiros, D., Santos, H., Silva, P., Madushanka, T., Matheswaran, K., Mutuwatte, L., Vickneswaran, K., Retief, H., Dickens, C., Garcia Andarcia, M. (2024). Operational SWAT+ Model: Advancing Seasonal Forecasting in the Limpopo River Basin. Colombo, Sri Lanka:  International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation.\n",
-    "- Mallick, Archita & Ghosh, Surajit & De Sarkar, Kounik & Roy, Sudip. (2024). Reservoir Water Level Forecasting using Deep Learning Technique. Conference: 2024 IEEE India Geoscience and Remote Sensing Symposium"
+    "- Retief, H., Andarcia, M. G., Dickens, C., & Ghosh, S. (2025). Dam Volume Prediction Model Development Using ML Algorithms. arXiv preprint arXiv:2502.19989.\n",
+    "https://doi.org/10.48550/arXiv.2502.19989\n",
+    "\n",
+    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.https://hdl.handle.net/10568/151898\n",
+    "\n",
+    "- Chambel-Leitão, P.; Santos, F.; Barreiros, D.; Santos, H.; Silva, Paulo; Madushanka, Thilina; Matheswaran, Karthikeyan; Muthuwatta, Lal; Vickneswaran, Keerththanan; Retief, H.; Dickens, Chris; Garcia Andarcia, Mariangel. 2024. Operational SWAT+ model: advancing seasonal forecasting in the Limpopo River Basin. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 97p. https://hdl.handle.net/10568/155533\n",
+    "\n",
+    "- Maity, R., Srivastava, A., Sarkar, S. and Khan, M.I., 2024. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning. Applied Computing and Geosciences, 24, p.100206.https://doi.org/10.1016/j.acags.2024.100206\n",
+    "\n",
+    "- Pimenta, J., Fernandes, J.N. and Azevedo, A., 2025. Remote Sensing Tool for Reservoir Volume Estimation. Remote Sensing, 17(4), p.619.https://doi.org/10.3390/rs17040619"
    ]
   },
   {

diff --git a/Use_cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/03_dam_volume_ model_prediction.ipynb b/Use_cases/Limpopo_River_Basin/01_Dam_Volume_Prediction/03_dam_volume_ model_prediction.ipynb
@@ -28,9 +28,9 @@
    "source": [
     "\n",
     "## Description\n",
-    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling. \n",
+    "This notebook presents a workflow for predicting dam levels and volumes using water surface area data from DE Africa's Waterbodies product, integrating data preprocessing, feature extraction, and Gradient Boosting modeling [(Retief et al., 2025)](https://arxiv.org/abs/2502.19989). \n",
     "\n",
-    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype Digital Twin for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
+    "As part of the CGIAR Initiative on Digital Innovation, this work contributes to a prototype [Digital Twin](https://digitaltwins.demos-only.iwmi.org/) for the Limpopo River Basin, designed to support real-time decision-making in water management. The Digital Twin leverages AI-driven tools to visualize and simulate the impact of decisions on the basin's ecosystem. To enhance prediction reliability, the model includes a correction mechanism to address unrealistic large drops in dam volume estimates.\n"
    ]
   },
   {
@@ -523,10 +523,16 @@
    "metadata": {},
    "source": [
     "**References:**\n",
-    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.\n",
-    "- Gurusinghe, T., Muthuwatta, L., Matheswaran, K., & Dickens, C. (2024). Developing a foundational hydrological model for the Limpopo River Basin using the Soil and Water Assessment Tool Plus (SWAT+). Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 14p.\n",
-    "- Leitão, P. C., Santos, F., Barreiros, D., Santos, H., Silva, P., Madushanka, T., Matheswaran, K., Mutuwatte, L., Vickneswaran, K., Retief, H., Dickens, C., Garcia Andarcia, M. (2024). Operational SWAT+ Model: Advancing Seasonal Forecasting in the Limpopo River Basin. Colombo, Sri Lanka:  International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation.\n",
-    "- Mallick, Archita & Ghosh, Surajit & De Sarkar, Kounik & Roy, Sudip. (2024). Reservoir Water Level Forecasting using Deep Learning Technique. Conference: 2024 IEEE India Geoscience and Remote Sensing Symposium"
+    "- Retief, H., Andarcia, M. G., Dickens, C., & Ghosh, S. (2025). Dam Volume Prediction Model Development Using ML Algorithms. arXiv preprint arXiv:2502.19989.\n",
+    "https://doi.org/10.48550/arXiv.2502.19989\n",
+    "\n",
+    "- Garcia Andarcia, M., Dickens, C., Silva, P., Matheswaran, K., & Koo, J. (2024). Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.https://hdl.handle.net/10568/151898\n",
+    "\n",
+    "- Chambel-Leitão, P.; Santos, F.; Barreiros, D.; Santos, H.; Silva, Paulo; Madushanka, Thilina; Matheswaran, Karthikeyan; Muthuwatta, Lal; Vickneswaran, Keerththanan; Retief, H.; Dickens, Chris; Garcia Andarcia, Mariangel. 2024. Operational SWAT+ model: advancing seasonal forecasting in the Limpopo River Basin. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 97p. https://hdl.handle.net/10568/155533\n",
+    "\n",
+    "- Maity, R., Srivastava, A., Sarkar, S. and Khan, M.I., 2024. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning. Applied Computing and Geosciences, 24, p.100206.https://doi.org/10.1016/j.acags.2024.100206\n",
+    "\n",
+    "- Pimenta, J., Fernandes, J.N. and Azevedo, A., 2025. Remote Sensing Tool for Reservoir Volume Estimation. Remote Sensing, 17(4), p.619.https://doi.org/10.3390/rs17040619"
    ]
   },
   {