Your search found 5 records
1 Katopodes, N. D.; Tang, J.; Clemmens, A. J. 1990. Estimation of surface irrigation parameters. Journal of Irrigation and Drainage Engineering, 116(5):676-696.
(Location: IWMI-HQ Call no: PER Record No: H06875)
2 Katopodes, N. D.; Tang, J.. 1990. Self-adaptive control of surface irrigation advance. Journal of Irrigation and Drainage Engineering, 116(5):697-713.
(Location: IWMI-HQ Call no: PER Record No: H06876)
3 Slika, J. W. F.; Arroyo-Rodriguezb, V.; Aibac, S.-I.; Alvarez-Loayzad, P.; Alvese, L. F.; Ashton, P.; Balvanera, P.; Bastian, M. L.; Bellingham, P. J.; van den Berg, E.; Bernacci, L.; da Conceicao Bispo, P.; Blanc, L.; Bohning-Gaese, K.; Boeckx, P.; Bongers, F.; Boyle, B.; Bradford, M.; Brearley, F. Q.; Hockemba, M. B.-N.; Bunyavejchewin, S.; Matos, D. C. L.; Castillo-Santiago, M.; Catharino, E. L. M.; Chai, S.-L.; Chen, Y.; Colwell, R. K.; Robin, C. L.; Clark, C.; Clark, D. B.; Clark, D. A.; Culmsee, H.; Damas, K.; Dattaraja, H. S.; Dauby, G.; Davidar, P.; DeWalt, S. J.; Doucet, J.-L.; Duque, A.; Durigan, G.; Eichhorn, K. A. O.; Eisenlohr, P. V.; Eler, E.; Ewango, C.; Farwig, N.; Feeley, K. J.; Ferreira, L.; Field, R.; de Oliveira Filho, A. T.; Fletcher, C.; Forshed, O.; Franco, G.; Fredriksson, G.; Gillespie, T.; Gillet, J.-F.; Amarnath, Giriraj; Griffith, D. M.; Grogan, J.; Gunatilleke, N.; Harris, D.; Harrison, R.; Hector, A.; Homeier, J.; Imai, N.; Itoh, A.; Jansen, P. A.; Joly, C. A.; de Jong, B. H. J.; Kartawinata, K.; Kearsley, E.; Kelly, D. L.; Kenfack, D.; Kessler, M.; Kitayama, K.; Kooyman, R.; Larney, E.; Laumonier, Y.; Laurance, S.; Laurance, W. F.; Lawes, M. J.; do Amaral, I . L.; Letcher, S. G.; Lindsell, J.; Lu, X.; Mansor, A.; Marjokorpi, A.; Martin, E. H.; Meilby, H.; Melo, F. P. L.; Metcalfea, D. J.; Medjibe, V. P.; Metzger, J. P.; Millet, J.; Mohandass, D.; Montero, J. C.; de Morisson Valeriano, M.; Mugerwa, B.; Nagamasu, H.; Nilus, R.; Onrizal, S. O.-G.; Page, N.; Parolin, P.; Parren, M.; Parthasarathy, N.; Paudel, E.; Permana, A.; Piedade, M. T. F.; Pitman, N. C. A.; Poorter, L.; Poulsen, A. D.; Poulsen, J.; Powers, J.; Prasad, R. C.; Puyravaud, J.-P.; Razafimahaimodison, J.-C.; Reitsma, J.; dos Santos, J. R.; Spironello, W. R.; Romero-Saltos, H.; Rovero, F.; Rozak, A. H.; Ruokolainen, K.; Rutishauser, E.; Saiter, F.; Saner, P.; Santos, B. A.; Santos, F.; Sarker, S. K.; Satdichanh, M.; Schmitt, C. B.; Schongart, J.; Schulze, M.; Suganuma, M. S.; Sheil, D.; da Silva Pinheiro, E.; Sist, P.; Stevart, T.; Sukumar, R.; Sun, I.-F.; Sunderand, T.; Suresh, H. S.; Suzuki, E.; Tabarelli, M.; Tang, J.; Targhetta, N.; Theilade, I.; Thomas, D. W.; Tchouto, P.; Hurtado, J.; Valencia, R.; van Valkenburg, J. L. C. H.; Van Do, T.; Vasquez, R.; Verbeeck, H.; Adekunle, V.; Vieira, S. A.; Webb, C. O.; Whitfeld, T.; Wich, S. A.; Williams, J.; Wittmann, F.; Woll, H.; Yang, X.; Yao, C. Y. A.; Yap, S. L.; Yoneda, T.; Zahawi, R. A.; Zakaria, R.; Zang, R.; de Assis, R. L.; Luize, B. G.; Venticinque, E. M. 2015. An estimate of the number of tropical tree species. Proceedings of the National Academy of Sciences of the United States of America, 112(24):7472-7477. [doi: https://doi.org/10.1073/pnas.1423147112]
(Location: IWMI HQ Call no: e-copy only Record No: H047084)
4 Dai, C.; Tang, J.; Li, Z.; Duan, Y.; Qu, Y.; Yang, Y.; Lyu, H.; Zhang, D.; Wang, Y. 2022. Index system of water resources development and utilization level based on water-saving society. Water, 14(5):802. [doi: https://doi.org/10.3390/w14050802]
Water resources ; Water conservation ; Water use efficiency ; Economic development ; Water supply ; Domestic water ; Industrial water use ; Urbanization ; Mineral waters ; Ecological factors ; Indicators ; Sensitivity analysis ; Case studies / China / Jingyu County
(Location: IWMI HQ Call no: e-copy only Record No: H051043)
(2.05 MB) (2.05 MB)
The notion of a ‘Water-saving society’ may help China achieve sustainable development and high-quality development. In this paper, the concept of water resources development and utilization level is discussed from the perspective of a water-saving society, and an evaluation index system including 33 indicators is constructed. This paper takes the evaluation of water resources development and utilization level of Jingyu County from 2009 to 2018 as an example to verify the rationality of the indicator system of this study. Additionally, by changing the sensitivity analysis method of indicator weights, the indicators with greater influence on the evaluation results are screened to reduce the uncertainty of too many indicators and low correlation. The results show that the evaluation value of water resources development and utilization level in Jingyu County from 2009 to 2018 was improved from V to II, and the improvement of industrial and domestic water use efficiency and effectiveness improved the water resource problems in the study area. Sensitivity analysis showed that the sensitivity parameters are the degree of water resources development and utilization (8.7%), water consumption per CNY 10,000 of industrial value added (11.2%), water consumption per CNY 10,000 of GDP (9.3%), leakage rate of the urban water supply network (8.4%), per capita water resources (10.1%), per capita COD emissions (9.3%) and urbanization rate (8.2%).
5 Yang, R.; Feng, J.; Tang, J.; Sun, Y. 2024. Risk assessment and classification prediction for water environment treatment PPP projects. Water Science and Technology, 89(5):1264-1281. [doi: https://doi.org/10.2166/wst.2024.052]
Water treatment ; Public-private partnerships ; Risk assessment ; Risk management ; Water management ; Models ; Machine learning ; Social capital
(Location: IWMI HQ Call no: e-copy only Record No: H052727)
https://iwaponline.com/wst/article-pdf/89/5/1264/1381518/wst089051264.pdf
https://vlibrary.iwmi.org/pdf/H052727.pdf
https://vlibrary.iwmi.org/pdf/H052727.pdf
(0.91 MB) (928 KB)
Water treatment public–private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies.
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