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This work aims to evaluate water scarcity in Thi-Qar governorate, Iraq, based on GIS estimation, environmental data, climate-change effects, and detection of the changes in marshes over the last three decades (1991–2021). The methodology process included collecting and analysing the related data sets such as water quality indicators, surface water quantity, climatic data, and Landsat’s images. GIS-based data and spatial data were acquired from the USGS website. Arc GIS 10.4.1 software was used to create a hydrological analysis. The results showed that generally, in Iraq, the annual volume of water available per person is 1,390.95 m³/cap/year, which is lower than the threshold for water scarcity (1700 m³/cap/year). The average daily potable water per person in Thi-Qar governorate was 284 L/cap/day, lower than the general average daily potable water per person of Iraq (340 L/cap/day). Meanwhile, 6% of the months along 1998–2018 did not meet the water demands. Water quality tests exhibited some high amounts of pollutants in drinking water, e.g., biological pollution was recorded in 55% of the total number of annual samples. Landsat’s images illustrated a high variation in water areas of marshes over the selected period, whereas the highest marshes area was 1548.21 km² in 1991 compared to the lowest area, 65.45 km² found in 1999. To sum up, the research outcomes revealed that the study area faced a serious water scarcity, which had a negative impact on the local people. Also, this research offered a scientific view for the decision-makers to mitigate and manage the water scarcity problem. [ABSTRACT FROM AUTHOR]

Change is considered a prominent feature of urban areas because of the various services and functions in cities, with complex functional relations. The study aims to build an informational foundation for urban planners and designers on the effects resulting from the imbalance in the organization and proportions of urban land uses, which in turn affects the performance of its functions in the city and its changes, and thus the necessity of using modern technologies to identify and define urban problems in cities, especially those related to land uses. An analytical approach to the research was taken, adopting geographic information systems as a tool for research by studying the reality of the region and determining the changes occurring from the functional side for the periods of 2,003,200,820,132,018. These periods witnessed substantial development and changes at the city and country levels. Through the results of the study, the changes were identified, remedies suggested, positive aspects were strengthened, and negative phenomena were reduced. The conclusions reached by the study indicate that planning for the use of the land is a very important practical aspect of achieving the needs of the population and limiting the noncultural aspects of expansion. Deliberate and planned change can generate positive results and vice versa. [ABSTRACT FROM AUTHOR]

The article tried to study the public transport role as one of the means that contribute to sustainable urban development by investigating the effect of the coverage rate for public transport on the number of those who use public transport in the neighbourhoods of Kut city. This article investigates the link between public transportation and supportive urban design trends. The research spatial and tem- poral limits are represented by the municipal boundaries Al-Kut city, which are shown in the master plan of the city for the period 2008–2012. The results showed a positive but weak relationship between public transport (variable factor) and the proportion of public transport coverage and population density (independent fac- tors). These factors do not explain the apparent, which is confirmed by the high Bo value and its great reliability, and this result is inconsistent with the hypothesis that states a positive effect between public transport and the coverage rate. However, this relationship is abnormal and indicates an imbalance in the distribution of land uses and the provision of services in the structure of Kut city. Furthermore, it is a negative indicator of the city’s structure, where a good and efficient structure requires a strong relationship. As for public transport passengers, they seem to be restricted by this for poor services. [ABSTRACT FROM AUTHOR]

The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. [ABSTRACT FROM AUTHOR]

Salinity is a classic problem in water quality management since it is directly associated with low water quality indices. Debate continues about selecting the best model for water quality forecasting, it remains a major challenge and causes much uncertainty. Accordingly, identifying the optimal modelling that can capture the salinity behaviour is becoming a common trend in recent water quality research. This study applies novel combined techniques, including data pre- processing and artificial neural network (ANN) optimised with constriction coeffi- cient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA) to forecast monthly salinity data. Historical monthly total dissolved solids (TDS) and electrical conductivity (EC) data of the Euphrates River at Al- Musayyab, Babylon, and climatic factors from 2010 to 2019 were used to build and validate the methodology. Additionally, for more validation, the CPSOCGSA-ANN was compared with the slime mould algorithm (SMA-ANN), particle swarm optimisation (PSO-ANN) and multi-verse optimiser (MVO-ANN). The results reveal that the pre-processing data approaches improved data quality and selected the best predictors’ scenario. The CPSOCGSA-ANN algorithm is the best based on several statistical criteria. The proposed methodology accurately simulated the TDS and EC time series based on R² = 0.99 and 0.97, respectively, and SI = 0.003 for both parameters. [ABSTRACT FROM AUTHOR]