Water sharing within the states/provinces of a country and cross-border is unavoidable. Conflicts between the sharing entities might turn more severe due to additional dependency on water, growing population, and reduced availability as a result of climate change at many locations. Pakistan, being an agricultural country, is severely water stressed and heading toward a worsening situation in the near future. Pakistan is heading toward water scarcity as water availability in the Indus basin is becoming critical. Being a downstream riparian of India and Afghanistan in the Indus basin, water availability depends on the releases of water from both countries. The Indus Water Treaty is governing the water distribution rights between India and Pakistan. However, there exists no proper agreement between Pakistan and Afghanistan and the construction of new dams on the Kabul River is another threat to water availability to Pakistan. Correct implementation of the Indus Water Treaty with India is required, together with an effective agreement with Afghanistan about the water sharing. In addition to water shortage, poor management of water resources, inequitable sharing of water, lack of a systematic approach, old-fashioned irrigation practices, and growing agricultural products with large water footprints are all exacerbating the problem. The water shortage is now increasingly countered by the use of groundwater. This sudden high extraction of groundwater is causing depletion of the groundwater table and groundwater quality issues. This water shortage is exacerbating the provincial conflicts over water, such as those between Punjab and Sindh provinces. At one end, a uniform nationwide water allocation policy is required. At the same time, modern irrigation techniques and low-water-footprint agricultural products should be promoted. A fair water-pricing mechanism of surface water and groundwater could be an effective measure, whereas a strict policy on groundwater usage is equally important. Political will and determination to address the water issues are required. The solutions must be based on transparency and equity, by using engineering approaches, combined with comprehensive social support. To develop a comprehensive water strategy, a dedicated technopolitical institute to strengthen the capabilities of nationwide expertise and address the issues on a regular basis is required to overcome the complex and multidimensional water-related problems of the country.

A Smart City is a solution to the problems caused by increasing urbanization. Australia has demonstrated a strong determination for the development of Smart Cities. However, the country has experienced uneven growth in its urban development. The purpose of this study is to compare and identify the smartness of major Australian cities to the level of development in multi-dimensions. Eventually, the research introduces the openings to make cities smarter by identifying the focused priority areas. To ensure comprehensive coverage of all aspects of the smart city’s performance, 90 indicators were selected to represent 26 factors and six components. The results of the assessment endorse the impacts of recent government actions taken in different urban areas towards building smarter cities. The research has pointed out the areas of deficiencies for underperforming major cities in Australia. Following the results, appropriate recommendations for Australian cities are provided to improve the city’s smartness.

Despite spending ample resources and procedural development in flood management, flood losses are still increasing worldwide. The losses caused by floods and costs incurred on management are two components of expected annual damages (EAD) due to floods. This study introduces a generalized approach for risk-based design where a range of probable floods are considered before and after a flood mitigation measure is implemented. The proposed approach is customized from the ISO Guide 31000 along with additional advantages of flood risk visualization. A Geographic Information System (GIS)-based design of a flood-protection dike is performed to exhibit the risk redistribution. The Chenab River is selected for the existing dike system. Detailed hazard behaviour and societal vulnerability are modelled and visualized for a range of all probable floods before and after the implementation of flood-protection dikes. EAD maps demonstrate the redistribution of induced and residual risks. It can be concluded that GIS-based EAD maps not only facilitate cost-effective solutions but also provide an accurate estimate of residual risks after the mitigation measures are applied. EAD maps also indicate the high-risk areas to facilitate designing secondary measures.

Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data bases to identify the anatomic risk factors for the OSA. Our research aims to develop a context-based automatic segmentation algorithm to delineate the fat pads from magnetic resonance images in a population-based study. Our segmentation pipeline involves texture analysis, connected component analysis, object-based image analysis, and supervised classification using an interactive visual analysis tool to segregate fat pads from other structures automatically. We developed a fully automatic segmentation technique that does not need any user interaction to extract fat pads. Our algorithm is fast enough that we can apply it to population-based epidemiological studies that provide a large amount of data. We evaluated our approach qualitatively on thirty datasets and quantitatively against the ground truths of ten datasets resulting in an average of approximately 78% detected volume fraction and a 79% Dice coefficient, which is within the range of the inter-observer variation of manual segmentation results. The suggested method produces sufficiently accurate results and has potential to be applied for the study of large data to understand the pathogenesis of the OSA syndrome.

Due to population growth, climatic change, and growing water usage, water scarcity is expected to be a more prevalent issue at the global level. The situation in Australia is even more serious because it is the driest continent and is characterized by larger water footprints in the domestic, agriculture and industrial sectors. Because the largest consumption of freshwater resources is in the agricultural sector (59%), this research undertakes a detailed investigation of the water footprints of agricultural practices in Australia. The analysis of the four highest water footprint crops in Australia revealed that the suitability of various crops is connected to the region and the irrigation efficiencies. A desirable crop in one region may be unsuitable in another. The investigation is further extended to analyze the overall virtual water trade of Australia. Australia’s annual virtual water trade balance is adversely biased towards exporting a substantial quantity of water, amounting to 35 km³, per trade data of 2014. It is evident that there is significant potential to reduce water consumption and footprints, and increase the water usage efficiencies, in all sectors. Based on the investigations conducted, it is recommended that the water footprints at each state level be considered at the strategic level. Further detailed analyses are required to reduce the export of a substantial quantity of virtual water considering local demands, export requirements, and production capabilities of regions.

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (R_a), water inflow (I_w), minimum water reservoir level (L_r), and storage capacity of the reservoir (C_r) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of R_a, I_w, L_r, and C_r. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment.