Background: Many rural electrification projects around the world employ micro hydropower plants (MHPs). These installations provide immediate and direct benefits to the local people. However, the sustainability of their operation in the long run remains a vital issue. Without proper sustainability assessment, the projects may face operational problems. However, to date, only a few empirical studies exist which offer tools to assess sustainability of MHP projects post-implementation. Given that every site has peculiar characteristics that could largely vary from site to site, there is a need to develop a model which could assess and compare the feasibility of the projects from the sustainability point of view before the project is implemented. For this purpose, a thorough sustainability assessment model was developed for an MHP project in a mountainous region of Nepal. Methods: This paper presents a sustainability assessment model for micro hydropower plants. In order to collect the data necessary to run the model, different sets of questionnaires were prepared for all relevant stakeholders. The developed model was used to assess an overall sustainability of a 26-kW plant at Mahadevsthan in Dhading District of Nepal. At this site, 15 community households, a project management committee member, an operator, and three policy makers/micro hydro experts were interviewed. The indicator system developed here was finalized with the stakeholder's participation. Results: A sustainability assessment model for the operation of micro hydropower plants in a remote rural area of Nepal was developed. Our model includes 54 assessment indicators taking into account economic, social, environmental, and technical sustainability dimensions and a scoring system (ranging from 1 to 5, with 5 being the best). It was found that the social dimension shows the best performance with a score of 4.17 for the studied MHP, followed by environmental (3.94), economic (3.74), and technical dimensions (3.04). Conclusions: The results show that the developed model creates a qualitative and quantitative basis for sustainability assessment of MHPs, allowing easiness for comparison of micro hydro projects, providing an effective decision-making support tool in rural electrification and development sector. The input of all stakeholders in identifying site-specific indicators that are relevant to the sustainability of the projects is crucial for minimizing biases in the assessment framework.
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify and localize underwater events. In this paper, we describe the technology and cognitive AI architecture of the system based on one of the sensor networks, the hydrophone network. We discuss the challenges of installing and using the hydrophone network in a water reservoir where traffic, visitors, and variable water conditions create a complex, varying environment. Our AI solution uses an autoencoder for unsupervised learning of latent encodings for classification and anomaly detection, and time delay estimates for sound localization. Finally, we present the results of experiments carried out in a laboratory pool and the water reservoir and discuss the system's potential. CCS Concepts: • Computing methodologies → Neural networks; Anomaly detection; • Computer systems organization → Sensor networks; • Hardware → Sensor applications and deployments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.