Your search found 8 records
1 Das, A.. 1997. Spatially varied flow over an embankment side weir. Journal of Irrigation and Drainage Engineering, 123(4):314-317.
(Location: IWMI-HQ Call no: PER Record No: H021007)
2 Das, A.; Datta, B. 1999. Development of management models for sustainable use of coastal aquifers. Journal of Irrigation and Drainage Engineering, 125(3):112-121.
Aquifers ; Salinity control ; Pumping ; Models ; Optimization ; Water resource management ; Sustainability
(Location: IWMI-HQ Call no: PER Record No: H024524)
3 Jain, S. K.; Das, A.; Srivastava, D. K. 1999. Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, 125(5):263-271.
Reservoir operation ; Operating policies ; Forecasting ; Constraints ; Sensitivity analysis ; Simulation models ; Irrigation water ; Electricity supplies ; Regression analysis / India / Orissa / Indravati Multipurpose Project
(Location: IWMI-HQ Call no: PER Record No: H024819)
4 Das, A.. 2000. Optimal channel cross section with composite roughness. Journal of Irrigation and Drainage Engineering, 126(1):68-72.
(Location: IWMI-HQ Call no: PER Record No: H025671)
5 Das, A.. 2007. Flooding probability constrained optimal design of trapezoidal channels. Journal of Irrigation and Drainage Engineering, 133(1):53-60.
(Location: IWMI HQ Call no: PER Record No: H040017)
6 Kuppannan, Palanisami; Das, A.. 2013. Water management options in the hill regions of Uttarakhand [India]. In Palanisami, Kuppannan; Sharda, V. N.; Singh, D. V. (Eds.). Water management in the hill regions: evidence from field studies. [Outcome of the IWMI and ICAR Workshop organized by IWMI-TATA Water Policy Research Program]. New Delhi, India: Bloomsbury Publishing India. pp.72-94.
Highlands ; Water management ; Water resources ; Climatic zones ; Rain ; Drainage ; Agricultural production ; Yield gap ; Irrigated land ; Irrigation systems ; Supplemental irrigation ; Microirrigation ; Legal aspects ; Economic aspects ; Costs ; Research programmes / India / Uttarakhand
(Location: IWMI HQ Call no: 333.91 G635 PAL Record No: H045729)
(1.36 MB)
7 Das, A.; Buisson, Marie-Charlotte; Mukherji, A. 2015. Predicting success in community-driven water infrastructure maintenance: evidence from public goods games in coastal Bangladesh. In Humphreys, E.; Tuong, T. P.; Buisson, Marie-Charlotte; Pukinskis, I.; Phillips, M. (Eds.). Proceedings of the CPWF, GBDC, WLE Conference on Revitalizing the Ganges Coastal Zone: Turning Science into Policy and Practices, Dhaka, Bangladesh, 21-23 October 2014. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food (CPWF). pp.183-196.
Coastal area ; Water management ; Local communities ; Sustainability ; Econometrics ; Models ; Institutions ; Reclaimed land / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H047112)
(0.31 MB)
8 Das, A.. 2024. Evaluation of prospective surface water potential zones and their suitability for drinking purposes in Mahanadi River Basin, Odisha (India) AQUA - Water Infrastructure, Ecosystems and Society, jws2024111. (Online first) [doi: https://doi.org/10.2166/aqua.2024.111]
Drinking water ; Surface water ; Water potential ; Water quality ; Decision making ; Machine learning ; Models ; Monitoring ; Geographical information systems ; Water management / India / Odisha / Mahanadi River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052756)
https://iwaponline.com/aqua/article-pdf/doi/10.2166/aqua.2024.111/1402217/jws2024111.pdf
https://vlibrary.iwmi.org/pdf/H052756.pdf
https://vlibrary.iwmi.org/pdf/H052756.pdf
(1.75 MB) (1.75 MB)
This study presents the usefulness of the water quality index (WQI) based on Fuzzy (F)-analytic hierarchy process (AHP), multi-criteria decision-making technique, namely, weighted sum approach (WSA) and machine learning models such as Borda scoring algorithm (BSA) for its evaluation and were further applied to the datasets on water quality (WQ) of the Mahanadi River (Odisha), generated during 5 years (2018–2023) of monitoring at 19 different sites for 20 parameters. The results render two parameters, namely coliform and TKN, exceeding the WHO standards. The results revealed that 52.63% of surface water samples are excellent in terms of drinking WQ, 26.32% of the samples are categorized under medium, and rest 21.05% are grouped under poor/very poor/unsuitable in terms of the F-AHP WQI. According to the results of WSA, 10 samples (52.63%) are low polluted zones, 6 samples (31.58%) are medium-polluted zones, and around 15.79% (3 samples) are highly polluted. The graphic representations obtained by BSA underline that the calculated value ranged between 15 and 256, stating in a zone of good to poor WQ. The best WQ was observed in T-(1), (5), (14), (15), (16), (17), and (18) because there were no changes in land use.
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