Smart City Gnosys
Smart city article details
| Title | Modelling Inters Seasonal Variability Impact On Water Demand In A Smart City |
|---|---|
| ID_Doc | 37685 |
| Authors | Mukome B.; Amoo O.; Seyam M. |
| Year | 2024 |
| Published | Lecture Notes in Networks and Systems, 1050 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-64847-2_4 |
| Abstract | Urbanisation development has evolved many modern challenges that demand continuous Smart City Configuration (SCC). This study explores online data mining with a multistage prospective design to model how climatic variables and the demography factor combined influence water demand in developing a water resources utilisation model for a smart city. The multiple logistic regression (MLR) models were used to analyse/model the water demand for the study area of Mthatha in the Eastern Cape Province in South Africa. The water demand is modelled as a function of the climatic variables and demographic factors. Various seasonal indices were used to assess the inter and intra-seasonal annual variability impacts on the available water. The results show a greater variation between 0.45 and 0.67 for demography explanatory variables while abrupt changes in rainfall trends and patterns show a coefficient of variation of 0.75 as a major factor required in smart city design. The result of the MLR water demand model predictor shows demography explanatory variables to be of greater influence than inter-seasonal rainfall fluctuation on the city water usage. Hydrologists and urban managers would find usefulness in the study towards decision-making in designing risk mitigation against drought for a smart city. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords | Climate change; data mining; drought; smart city; water demand |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
24067  | 0.87 | Singh S.; Jain V.; Goyal M.K. | Enhancing Urban Resilience Against Elevation-Driven Precipitation Risks In Indian Smart Cities | Urban Climate, 62 (2025) |
| 40687 | 0.86 | Akhayeva Z.B.; Zakirova A.B.; Zhmud V.A. | Optimization Of Water Resources Management Using Big Data And Machine Learning In Smart Cities | Eurasian Journal of Mathematical and Computer Applications, 12, 2 (2024) |
| 10905 | 0.859 | Bui M.T.; Yáñez-Godoy H.; Elachachi S.M. | Assessment Of The Implications And Challenges Of Using Artificial Intelligence For Urban Water Networks In The Context Of Climate Change When Building Future Resilient And Smart Infrastructures | Journal of Pipeline Systems Engineering and Practice, 16, 1 (2025) |
| 39987 | 0.859 | Herrera, M; Izquierdo, J; Pérez-García, R; Ayala-Cabrera, D | On-Line Learning Of Predictive Kernel Models For Urban Water Demand In A Smart City | 12TH INTERNATIONAL CONFERENCE ON COMPUTING AND CONTROL FOR THE WATER INDUSTRY, CCWI2013, 70 (2014) |
| 57311 | 0.854 | Kumar Goyal M.; Poonia V.; Jain V. | Three Decadal Urban Drought Variability Risk Assessment For Indian Smart Cities | Journal of Hydrology, 625 (2023) |
| 22932 | 0.854 | Ghanim A.A.J.; Shaf A.; Irfan M.; Alzabari F.; Magzoub Mohamed Ali M.A. | Empowering Smart Cities: Leveraging Advanced Forecasting Models For Proactive Rainfall Prediction And Resilient Urban Planning | AIP Advances, 15, 7 (2025) |
| 13118 | 0.853 | Van Belle J.-P.; Hlabano M. | Building Urban Resilience In The Face Of Severe Drought Through The Innovative Use Of Open Data | 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019 (2019) |