Sulaimania Journal for Engineering Sciences

The aim of this study is to propose an index of Fuzzy Water Quality Index (FWQI), to assess the water quality of Qilyasan Stream. The suggested fuzzy index incorporates the specified thresholds for quality indicators, which are primarily taken from Iraqi water laws. Eighteen parameters are combined in the suggested index including temperature, pH, alkalinity, total hardness, EC, TDS, TSS, turbidity, NO3, SO4, PO4, BOD, COD, DO, Pb, Cd, Zn, and Cu. MATLAB R2022b was used to develop the fuzzy model. Five stations in the stream were selected for sampling. These membership-grade classifications can be a useful tool for decision-making, helping to designate a progressive quality sub-objective to each stream segment. The obtained results from FWQI were compared with Weighted Arithmetic Water Quality Index (WAWQI). The results revealed that the FWQI at St 1, St 2, and St 5 is (9.78), and at St 3, and St 4 is (10.1), the quality ranking of all stations was very poor, and WAWQI shows that all stations were unsuitable reusing purposes. The proposed approach shows a convenient tool for monitoring of stream waters.

Soil is a great reservoir of nutrients and pollutants, and as such plays an important role in health and socio-ecological sustainability. Contaminants from agriculture, urbanization and industrial activities enter the soil which increases soil pollution and impose a big risk on human health. Soil samples from two different locations (Kostay Cham and Kani Goma) in Sulaymaniyah city were examined. The areas are two agricultural farms irrigated by water from stream and by wastewater respectively. Six different points were selected at two study areas at surface and at 60 cm depth for each point. While two water samples were collected at each site (pH, electric conductivity EC, DO, BOD5, TDS, TSS, salinity, turbidity, total alkalinity and hardness, with heavy metals Pb, Cd, Cu, Ni, Fe, and Zn) in Qilyasan stream water and wastewater determined. This study introduces a novel Combined Pollution Quality Index (CPQI) that integrates six weighted Indices-Nutrient Index, Salinity Index, Organic Matter Index, Sodium Adsorption Ratio (SAR) Index, Heavy Metal Risk Index, and Pollution Quality Index (PQI). Analytic Hierarchy Process (AHP) used to find weight values for each index. Chemical, physical and biological tests were carried for all soil samples such as; heavy metals, organic matter, nutrients, PH, TDS, EC. The results showed that the heavy metal concentration in all samples (surface and 60 cm depth) are within the allowable range. Generally higher levels of most heavy metals observed at 60 cm depth compared to surface samples. CPQI (combined pollution quality index) derived from all indices together to realize pollution index for each location, consequently all the CPQI values founded are below 1, meaning they fall under "Low Pollution" for all selected sites condition according to the limitations values, the highest value taken by the first location 60 cm deep result about 0.49 and lowest value 0.34 for the first location surface sample. this result is due to the high probability of absorption by plant roots and vegetables.

Mosul, the second most populated city in Iraq, has been growing irregularly for almost two decades. The pattern of this sprawl shows imbalance in the city’s growth. The study mostly focusing on the urban structure of the city, includes indicators of social, economic, and political irregularities. This study used a mixed-method approach to reach its findings, by applying more than a test. The study aims to check how this irregularity is taking place in Mosul, by studying the geographical and the syntactic models. The study is an analytical one and has a descriptive aim. It checked the indicators of imbalanced growth by observing the city’s growth through three decades. Satellite images were used to observe the growth, and then a field survey of the distribution of the most important elements (such as schools and hospitals) was conducted. Educational and healthcare facilities represent the most important infrastructure needed for any city to grow, and their distribution patterns can show if the growth patterns of a city are regular or not. Axial segment analysis, part of Space Syntax analyses, was also used to check the integration values of the local cores in the city and how they changed through time. The results show clear indicators of unbalanced growth and distribution of facilities across the city. The results also indicate irregularity in the city’s growth through the last three decades. The study shows that the Eastern bank of the city is growing three times faster than the western bank, and that the Eastern bank has 1.5 to 2 times more health and education infrastructure. Space Syntax showed that the values of integration for the local cores are becoming weaker as the city is expanding. The maps produced by the study can prove useful for any party trying to develop a new masterplan for the city.

A neural network model was used to categories and predict the weather in Sulaymaniyah, which is located in the Kurdistan Region of Iraq. The use of neural network models (NNM) for climate data analysis has advanced; the accuracy and application of neural network models for climate data analysis have significantly advanced in recent years. Using NNM affected the prediction; the dataset-based forecasting approach uses a single-column time series to depict the future. A real-time weather station dataset from the Sulaimani Meteorological and Seismological Directorate is used in the implementation model. The data collection includes implementation data from the weather station for an iterative neural network model that forecasts future climate and displays historical propagation. From 1993 to 2023, the dataset includes daily data on average temperature, humidity percentage, and precipitation for each of the twelve months from January to December. It was gathered every day for thirty years and includes information about Suleimani City's maximum and minimum temperatures, average temperatures, humidity levels, and rainfall over that time. The models were used to predict relative average temperatures. As the results indicate that Bi-LSTM and GRU outperformed GBT in both training and testing.