Your search found 3 records
1 Al-Kaisi, M. M.; Yin, X.. 2003. Effects of nitrogen rate, irrigation rate, and plant population on corn yield and water use efficiency. Agronomy Journal, 95:1475-1482.
Irrigation management ; Water quality ; Nitrogen ; Maize ; Crop production ; Yields ; Soil moisture ; Water use efficiency / USA / Great Plains / Ogallala Aquifer
(Location: IWMI-HQ Call no: P 7045 Record No: H035599)
(0.08 MB)
2 Rasool, U.; Yin, X.; Xu, Z.; Rasool, M. A.; Senapathi, V.; Hussain, M.; Siddique, J.; Trabucco, J. C. 2022. Mapping of groundwater productivity potential with machine learning algorithms: a case study in the provincial capital of Baluchistan, Pakistan. Chemosphere, 303(Part 3):135265. [doi: https://doi.org/10.1016/j.chemosphere.2022.135265]
Groundwater potential ; Water productivity ; Mapping ; Machine learning ; Case studies ; Assessment ; Water quality ; Remote sensing ; Geographical information systems ; Neural networks ; Models / Pakistan / Baluchistan / Quetta
(Location: IWMI HQ Call no: e-copy only Record No: H051222)
(7.42 MB)
Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.
3 Almouctar, M. A. S.; Wu, Y.; An, S.; Yin, X.; Qin, C.; Zhao, F.; Qiu, L. 2024. Flood risk assessment in arid and semi-arid regions using Multi-criteria approaches and remote sensing in a data-scarce region. Journal of Hydrology: Regional Studies, 54:101862. [doi: https://doi.org/10.1016/j.ejrh.2024.101862]
(Location: IWMI HQ Call no: e-copy only Record No: H052972)
https://www.sciencedirect.com/science/article/pii/S2214581824002106/pdfft?md5=9bc981e26ab20128bd37bfbd3f61145f&pid=1-s2.0-S2214581824002106-main.pdf
https://vlibrary.iwmi.org/pdf/H052972.pdf
https://vlibrary.iwmi.org/pdf/H052972.pdf
(4.83 MB)
Flooding is a natural disaster that poses a threat to both people and the environment, necessitating proactive assessment and mitigation strategies to protect vulnerable communities and ecosystems. These measures are necessary to reduce the risk of flooding and moderate the impact of rainfall. In this study, an Analytical Hierarchy Process (AHP) was used to evaluate flood risk in a data-limited region by integrating Remote Sensing (RS) and Geographic Information System (GIS) methods. The study identified several key flood risk indicators, including topographic wetness index, elevation, slope, land cover, precipitation, distance to river, distance to road, and NDVI. The flood risk map had a score range of 8.71–30.99 %, with higher scores indicating a greater susceptibility to flooding. These scores were then used to classify the flood risk into five categories: very low, low, moderate, high, and very high. The percentages of regions falling into each category were 8.71 %, 23.52 %, 30.99 %, 22.68 %, and 14.09 % respectively. The area under the Curve (AUC) approach was used to validate the flood risk map, which showed a high degree of accuracy (0.86). The results of this study provide valuable insights for monitoring, and forecasting the probability of floods in the Dosso Region.
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