Article ID: | iaor20083295 |
Country: | Canada |
Volume: | 7 |
Issue: | 1 |
Start Page Number: | 1 |
End Page Number: | 13 |
Publication Date: | Jan 2006 |
Journal: | Journal of Environmental Informatics |
Authors: | Baetz B.W., Olsen O.R., Dickson S.E. |
Keywords: | developing countries, artificial intelligence: decision support |
A considerable amount of knowledge exists for planning, designing, and implementing rural water supply schemes in developing countries around the world. Generalized decision support systems (DSSs) are used to tackle the daunting task of providing water in areas that have poor water resources and limited financial capacity. However, there is a lack of site-specific DSSs that utilise local hydrological and socio-economic data for assessing regionally-based rural water supply schemes. In the Nilgiris District of South India, an organizational structure exists for improving local water resources through watershed management projects, but many of the tools used to make informed decisions are ineffective, leading to poorly designed systems. Addressing this problem, a prototype DSS called Nilgiris Rural Water Supply (NRWS) was developed for rural water supply in the Nilgiris District. It assists planners in developing a decision-matrix for potential water sources based on six criteria: water source yield; capital costs; cost and ease of operation and maintenance; impact of development; political/legal constraints; and water quality. Within the water source yield criterion, a simulation method was used to develop design specifications for the rainwater harvesting (RWH) source. A general application of the rooftop RWH simulation was applied to ten villages throughout the Nilgiris District. One important discovery was that the village of Masinigudi, which lies in a rain shadow and receives the lowest level of annual rainfall in the district, performed to the same level as villages with a high annual rainfall. Since the region surrounding Masinigudi is deprived of water sources, such as mountain streams and dug wells, RWH was found to be a feasible and economically viable solution.