The aim of the study was to analyse the causes of the flooding problems being encountered in Lagos (Nigeria) and to recommend sustainable management solutions to them. Data on climate, drainage infrastructures and physical planning regulations were collected and extensively analysed. These were combined with evidence from field inspection and discussion with stakeholders, including relevant government departments, university researchers and selected residents. The investigation revealed that, contrary to popular wisdom, climate change or unusually high rainfall is not the primary cause of the flooding problems in Lagos. Rather, the increased urbanisation, lax planning laws in relation to the erection of buildings in flood plains and the lack or inadequacy of storm drainage facilities in the city are to blame. It is argued that a lasting solution to the flooding problem will require the incorporation of sustainable drainage systems within the existing flood management strategy for the city and planning for this must start now.
Consumption of water is never constant throughout the day due to the daily routines of the consumer. This pattern of daily water consumption is called water demand profile. The initiative to create these profiles are to improve hydraulic performance and to build energy conservative strategies for designed networks in Dubai. Therefore, the aim is to develop and analyze a domestic consumption profile for selected developments with socio-demographic factors including weekday/weekend variation, population, income, fasting during the month of Ramadan, and the outbreak of Covid-19. Data from more than 7000 smart meters were collected while water meters of more than 350 residential flats were examined manually. Water demand profiles generated from the data showed weekdays have more predictable peaks (morning 6–8 am and evening 5–7 pm) than weekends. During Ramadan, peak hours shifted to 7–10 am followed by 3–4 pm during workdays while peaks for low income areas are higher due to stricter working routine. The Covid-19 crisis has led to significant rise in observed consumption, with over 30% increase during the month of Ramadan. The observed results, if compared with further end-use studies on more factors affecting demand profiles, can help in generating both cost and energy efficient networks.
[1] Reference crop evapotranspiration (ET o ) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfallrunoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman-Monteith benchmark model, are timeconsuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self-organizing map (KSOM), unsupervised artificial neural networks, to predict the ET o. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ET o (based on the Food and Agricultural Organization (FAO) Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM-based ET o estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM-based estimates were also found to be significantly superior to those estimated using currently recommended empirical ET o methods for data scarce situations such as those in developing countries.
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