Your search found 8 records
1 Hussain, M.. 1975. Changes in net income of areas being irrigated with different amounts of canal and tubewell water during 1970-71. Bhalwal, Pakistan: WAPDA. iv, 51p. (Water and Power Development Authority publication no.37)
(Location: IWMI-HQ Call no: 631.7.4 G730 HUS Record No: H0602)
2 Hussain, M.; Ali, B.; Johnson, S. H. III. 1976. Cost of water per acre foot and utilization of private tubewells in Mona Project SCARP - II. Bhalwal, Pakistan: WAPDA. ii, 19p. (Water and Power Development Authority publication no.62)
(Location: IWMI-HQ Call no: 631.7.4 G730 HUS Record No: H0601)
3 Hussain, M.; Ali, B.; Johnson, S. H. III. 1976. Socio economic Bench Mark Survey Tubewell No.56. Bhalwal, Pakistan: Directorate of Mona Reclamation Experimental Project. 27p. (Water and Power Development Authority publication no.58)
Water use efficiency ; Resource management ; Research ; Watercourses ; Tube wells ; Intensive cropping ; Labor / Pakistan
(Location: IWMI-HQ Call no: 631.7.6.3 G730 HUS Record No: H0605)
4 Hussain, M.. 2002. Socio-environmental concerns in dams: Pakistan’s scenario. In Pakistan Water Partnership (PWP). Second South Asia Water Forum, 14-16 December 2002, Islamabad, Pakistan. Proceedings, vol.1. Islamabad, Pakistan: Pakistan Water Partnership (PWP). pp.189-193.
(Location: IWMI HQ Call no: 333.91 G570 PAK Record No: H034139)
5 Hussain, M.. 2002. Water conservation and role of the youth. In Pakistan Water Partnership (PWP). Second South Asia Water Forum, 14-16 December 2002, Islamabad, Pakistan. Proceedings, vol.2. Islamabad, Pakistan: Pakistan Water Partnership (PWP). pp.533-536.
(Location: IWMI HQ Call no: 333.91 G730 PAK Record No: H034237)
6 Hussain, M.. 2004. Poverty among farming community in marginal areas of Punjab. In Jehangir, Waqar A.; Hussain, Intizar (Eds.). Poverty reduction through improved agricultural water management. Proceedings of the Workshop on Pro-poor Intervention Strategies in Irrigated Agriculture in Asia, Islamabad, Pakistan, 23-24 April 2003. Lahore, Pakistan: International Water Management Institute (IWMI). pp.137-144.
(Location: IWMI HQ Call no: IWMI 631.7.3 G730 JEH Record No: H043766)
Land resources having low productive potential than those in normal areas are treated as marginal areas. These marginal areas are mainly due to lack of irrigation facilities, uneven topography and bad soil structure. Due to low agricultural productivity, farmers are poor in the marginal areas. There are 1.8 million hectares of Potohar Plateau, 4.48 million hectares of Desert areas (Thal and Cholishtan), 3.31 million hectares of Hilly areas (Muree, Salt range, Siwalik range, D.G. Khan) and 1.23 million hectares of Riverine areas classified as marginal areas in Punjab (ABAD 1988). Agriculture is totally dependent on rainfall in these areas. The present study was carried out in Potohar Plateau to assess poverty situation among farming community. Two villages were selected from each of the tehsils (Jand, Gujar Khan and Attock) based on their location, one near the road and other at least 10 kilometers away from the main road. Ten farmers and five non-farmers from each village were chosen for this study. A relatively lower poverty incidence was measured for Jand tehsil in Attock district as compared to Gujar Khan tehsil of Rawalpindi district. Family size, dependency ratio, education of the household head, landholding and noncrop income were found as the major determinants of the poverty in marginal areas of Punjab, Pakistan
7 Hussain, M.; Ghafoor, A. 2013. Adoption of wheat production technologies in Pakistan: implications for food security and agricultural policy. In Hanjra, Munir A. (Ed.). Global food security: emerging issues and economic implications. New York, NY, USA: Nova Science Publishers. pp.231-243. (Global Agriculture Developments)
Crop production ; Wheat ; Technology ; Food security ; Agricultural policy ; Smallholders ; Socioeconomic environment ; Models / Pakistan
(Location: IWMI HQ Call no: e-copy only Record No: H046194)
(4.51 MB)
8 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.
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