New Fuzzy Performance Indices for Reliability Analysis of Water Supply Systems

Large and complex engineering systems are subject to wide range of possible future loads and conditions. Uncertainty associated with the quantification of these potential conditions is imposing a great challenge to systems‘ design, planning and management. Therefore, the assurance of satisfactory and reliable system performance cannot be simply achieved. Water supply systems, as typical example of these engineering systems, include collections of different types of facilities. These facilities are connected in complicated networks that extend over and serve broad geographical regions. As a result, water supply systems are at risk of temporary disruption in service due to natural hazards or anthropogenic causes, whether unintentional (operational errors and mistakes) or intentional (terrorist act). Quantification of risk is a pivotal step in the engineering risk and reliability analysis. In this analysis, uncertainty is measured using different system performance indices and figures of merit to evaluate its consequences for the safety of engineering systems. The probabilistic reliability analysis has been extensively used to deal with the problem of uncertainty in many engineering systems. However, application of probabilistic reliability analysis is invariably affected by the well-known engineering problem of data insufficiency. Bayesian approach and subjective probability estimation are used to evaluate, express, and communicate uncertainty that stems from lack of information or data unavailability. They introduce a formal procedure for incorporating subjective belief and engineering understanding together with the available data. Fuzzy set theory, on the other hand, was developed to try to capture people judgmental believes, or as mentioned before, the uncertainty that is caused by the lack of knowledge. Fuzzy set theory and fuzzy logic contributed successfully to the technological development in different application in real-world problems of different kinds, (Zimmermann, 1996). This study explores the utility of the fuzzy set theory in the field of engineering system reliability analysis. Three new fuzzy reliability measures are suggested: (i) reliability index, (ii) robustness index, and (iii) resiliency index. These measures are evaluated, together with fuzzy reliability measure developed by Shrestha and Duckstein (1998), using two simple hypothetical cases. The new suggested indices are proven to be able to handle different fuzzy representations. In addition, these reliability measures comply with the conceptual approach of the fuzzy sets.https://ir.lib.uwo.ca/wrrr/1007/thumbnail.jp

Application of the Fuzzy Performance Indices to the City of London Water Supply System

This study explores the utility of fuzzy performance indices: (i) combined reliabilityvulnerability index, (ii) robustness index, and (iii) resiliency index, for evaluating the performance of a complex water supply system. Regional water supply system for the City of London is used as the case study. The two main components being investigated in this case study are; (i) the Lake Huron Primary Water Supply System (LHPWSS), and (ii) the Elgin Area Primary Water Supply system (EAPWSS).https://ir.lib.uwo.ca/wrrr/1012/thumbnail.jp

A Decision Support System for Integrated Risk Management

This report provides a detailed description of the Risk Assessment Support System (RASS) for use in municipal water supply. The report explores the utility of the developed support system for evaluating the performance of a complex water supply system. A regional water supply system for the city of London is used as the case study. The theoretical foundations and computational requirements for the implementation of the RASS are provided in the report.https://ir.lib.uwo.ca/wrrr/1013/thumbnail.jp

Climate change impact and adaptation on wheat yield, water use and water use efficiency at North Nile Delta

This is an accepted manuscript of an article published by Springer in Frontiers of Earth Science on 29/04/2020, available online: https://doi.org/10.1007/s11707-019-0806-4 The accepted version of the publication may differ from the final published version.© 2020, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Investigation of climate change impacts on food security has become a global hot spot. Even so, efforts to mitigate these issues in arid regions have been insufficient. Thus, in this paper, further research is discussed based on data obtained from various crop and climate models. Two DSSATcrop models (CMs) (CERESWheat and N-Wheat) were calibrated with two wheat cultivars (Gemiza9 and Misr1). A baseline simulation (1981-2010) was compared with different scenarios of simulations using three Global Climate Models (GCMs) for the 2030s, 2050s and 2080s. Probable impacts of climate change were assessed using the GCMs and CMs under the high emission Representative Concentration Pathway (RCP8.5). Results predicted decreased wheat grain yields by a mean of 8.7%, 11.4% and 13.2% in the 2030s, 2050s and 2080s, respectively, relative to the baseline yield. Negative impacts of climatic change are probable, despite some uncertainties within the GCMs (i. e., 2.1%, 5.0% and 8.0%) and CMs (i.e., 2.2%, 6.0% and 9.2%). Changing the planting date with a scenario of plus or minus 5 or 10 days from the common practice was assessed as a potentially effective adaptation option, which may partially offset the negative impacts of climate change. Delaying the sowing date by 10 days (from 20 November to 30 November) proved the optimum scenario and decreased further reduction in wheat yields resulting from climate change to 5.2%, 6.8% and 8.5% in the 2030s, 2050s and 2080s, respectively, compared with the 20 November scenario. The planting 5-days earlier scenario showed a decreased impact on climate change adaptation. However, the 10-days early planting scenario increased yield reduction under projected climate change. The cultivar Misr1 was more resistant to rising temperature than Gemiza9. Despite the negative impacts of projected climate change on wheat production, water use efficiency would slightly increase. The ensemble of multi-model estimated impacts and adaptation uncertainties of climate change can assist decision-makers in planning climate adaptation strategies.Published versio

Assessment of Soil Capability and Crop Suitability Using Integrated Multivariate and GIS Approaches toward Agricultural Sustainability

Land evaluation has an important role in agriculture. Developing countries such as Egypt face many challenges as far as food security is concerned due to the increasing rates of population growth and the limited agriculture resources. The present study used multivariate analysis (PCA and cluster analysis) to assess soil capability in drylands, Meanwhile the Almagra model of Micro LEIS was used to evaluate land suitability for cultivated crops in the investigated area under the current (CS) and optimal scenario (OS) of soil management with the aim of determining the most appropriate land use based on physiographic units. A total of 15 soil profiles were selected to characterize the physiographic units of the investigated area. The results reveal that the high capability cluster (C1) occupied 31.83% of the total study area, while the moderately high capability (C2), moderate capability (C3), and low capability (C4) clusters accounted for 37.88%, 28.27%, and 2.02%, respectively. The limitation factors in the studied area were the high contents of CaCO3, the shallow soil depth, and the high salinity and high percentage of exchangeable sodium (% ESP) in certain areas. The application of OS enhanced the moderate suitability (S3) and unsuitable clusters (S5) to the suitable (S2) and marginally suitable (S4) categories, respectively, while the high suitability cluster (S1) had increased land area, which significantly affected the suitability of maize crop. The use of multivariate analysis for mapping and modeling soil suitability and capability can potentially help decision-makers to improve agricultural management practices and demonstrates the importance of appropriate management to achieving agricultural sustainability under intensive land use in drylands