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Sediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916. [ABSTRACT FROM AUTHOR]

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also. [ABSTRACT FROM AUTHOR]

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively. [ABSTRACT FROM AUTHOR]

This study intends to evaluate university students' awareness regarding water pollution in Iraq to show the environmental education contribution in reducing water pollution. The study was conducted in Wasit university, Wasit province, Iraq by field questionnaire survey with 388 students from the civil engineering department and engineering college. The questionnaires were divided according to the pollution causes into industrial, agricultural and sewage water pollutants. The data were analyzed utilizing the thematic analysis to assess environmental knowledge and awareness of water pollution risks. The results show that most students have high interest in environmental conservation, yet, a lack of education in general environmental education, as the educational curriculum focuses on theoretical aspects more than the practical ones. The results also show that the media role is inactive concerning environmental awareness of water pollution. This study provides a scientific view of policymakers for potential future conditions to find solutions that achieve sustainability goals. [ABSTRACT FROM AUTHOR]

The current study was conducted to determine the quality of the water of Al- Sabil River in Al-Shinafiya district, Province of Al- Diwaniyah, for the period from September 2018 to August 2019. Three sites were selected along the river, a quantitative and qualitative study of the diatoms as well as its indices to assess the quality of water in the river, such as trophic diatom Index (TDI), Trophic State Index (TSI), Diatomic Index (DI), and General Diatoms Index (GDI). The current study was diagnosed about 136 species of diatoms at three sites, where the central diatoms was 12 species while the pannals diatoms reached 124 species, and recorded total numbers of diatoms (35453.8, 29447.2 and 36504.76) cell*310/L, and rates (2954.48, 2453.93 and 3042.06) cells*310/L for the three locations respectively, as shown by the results of the trophic diatom Index(TDI) values ranged from (23.33 to 55.54) and the values of Trophic State Index (TSI) ranged from (0.07 to 0.81) and the Diatomic Index (DI) values ranged from (9.08 to 16.20) and the values of the General Diatoms Index (GDI) ranged from (2.23 to 3.17). It was noted that the water quality was Oligotrophic to Mesotrophic based on TDI and TSI, while its moderate to Good based on DI and GDI, and concluded that the higher the evidence values indicated that the water quality tended to be good, free or low pollution, suitable for living well, and that water was few nutrients, while its low values indicated the deterioration of the water quality in the river. [ABSTRACT FROM AUTHOR]

Estrogenic hormones, found as micropollutants in water systems, give rise to grave concerns for human health and marine ecosystems, triggering a cascade of adverse effects. This research presents an innovative manufacturing approach using nanoscale layered double hydroxides of magnesium and iron, with sodium dodecyl sulfate surfactant, to create highly efficient sorbent cement kiln dust (CKD) based beads (CKD/MgFe-SDS-LDH-beads). These beads effectively remove estrone from water. Optimization of the preparation process considered factors like molar Mg/Fe ratio, CKD dosage, pH, and SDS dosage using Response Surface Methodology (RSM). The adsorption process was well-characterized by Langmuir isotherm and pseudo-second-order kinetic models, demonstrating a remarkable 6.491 mg/g sorption capacity. Results proved that the calcite was the main component of the CKD with miners of dolomite, and quartz. Adsorption capacity, surface charges, and the availability of vacant sites may be the main mechanisms responsible of removal process. Experimental tests confirmed the beads' potential for estrone removal, aligning with the Bohart-Adams and Thomas-BDST models. This study introduces a promising, eco-friendly solution for addressing water contamination challenges. [Display omitted] • Using cement kiln dust byproducts for preparation of novel sorbent. • Sorbent is prepared in the form of sodium alginate beads. • Beads are composed of kiln dust, magnesium, iron and surfactant. • Beads are used to treat water laden with estrone. • Maximum adsorption capacity for prepared beads is 6.491 mg/g. [ABSTRACT FROM AUTHOR]

[Display omitted] • High-resolution prediction of air fine particular matter concentration is conducted. • New developed machine learning algorithms are adopted for this purpose. • Prediction for temporal/spatial variability of PM 2.5 over Iraq region is studied. • Research provided good forecasting spatial variability of PM 2.5 at high resolution. This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM 2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM 2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM 2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM 2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM 2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM 2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM 2.5 forecasting maps. [ABSTRACT FROM AUTHOR]

Documenting habitats of rangeland plant species is required to properly manage rangelands and to understand ecosystem processes. A reliable rangeland species potential map can help managers and policy makers design a sustainable grazing system on rangelands. The aim of this study is to map the plant species in the Qurveh City rangelands, Kurdistan Province, Iran, using state-of-the-art machine learning algorithms, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Bayes Net (BN) and Classification and Regression Tree (CART). A total of 185 rangeland species were used in the study, together with 20 conditioning factors, to build and validate models. The One-R feature section technique and multicollinearity test were used, respectively, to determine the most important factors and correlations between them. Model validation was performed using sensitivity, specificity, accuracy, F1-measure, Matthews correlation coefficient (MCC), Kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). Results showed that topographic wetness index (TWI), slope angle, elevation, soil phosphorus and soil potassium were the five most important factors to increase the rangeland plants habitat suitability. The Naïve Bayes algorithm (AUC = 0.782) had the highest performance and prediction accuracy and best consistency across the species in the investigated rangeland, followed by the SVM (AUC = 0.763), ANN (AUC = 0.762), CART (AUC = 0.627), and BN (AUC = 0.617) models. [Display omitted] • The habitats of important ecological rangeland plants were modeled and mapped. • Machine learning algorithms are robust tools in rangeland rehabilitation and management. • Topographic, phosphorus and potassium were the main factors to increase habitat suitability. • NB and CART had the highest and lowest prediction for studied species of investigated rangeland. • High potential rangeland habitats can help decision makers in better rangeland management. [ABSTRACT FROM AUTHOR]

Covid-19 was first reported in Iraq on February 24, 2020. Since then, to prevent its propagation, the Iraqi government declared a state of health emergency. A set of rapid and strict countermeasures have taken, including locking down cities and limiting population's mobility. In this study, concentrations of four criteria pollutants, NO 2 , O 3 , PM 2.5 and PM 10 before the lockdown from January 16 to February 29, 2020, and during four periods of partial and total lockdown from March 1 to July 24, 2020, in Baghdad were analysed. Overall, 6, 8 and 15% decreases in NO 2 , PM 2.5 , and PM 10 concentrations, respectively in Baghdad during the 1st partial and total lockdown from March 1 to April 21, compared to the period before the lockdown. While, there were 13% increase in O 3 for same period. During the 2nd partial lockdown from June 14 to July 24, NO 2 and PM 2.5 decreases 20 and 2.5%, respectively. While, there were 525 and 56% increase in O 3 and PM 10 , respectively for same period. The air quality index (AQI) improved by 13% in Baghdad during the 1st partial lockdown from March 1 to April 21, compared to its pre-lockdown. The results of NO 2 tropospheric column extracted from the Sentinel-5P satellite shown the NO 2 emissions reduced up to 35 to 40% across Iraq, due to lockdown measures, between January and July, 2020, especially across the major cities such as Baghdad, Basra and Erbil. The lockdown due to COVID-19 has drastic effects on social and economic aspects. However, the lockdown also has some positive effect on natural environment and air quality improvement. Unlabelled Image • NO 2 concentrations reduced by 6, 7, 8 and 20%, respectively in Baghdad during the lockdown. • O 3 concentrations increased by 13%, 75%, 225% and 525%, for the same periods. • AQI improved in Baghdad by 13%, compared to the pre-lockdown. • NO 2 emissions reduced up to 35 to 40% in Iraq compared to the pre-lockdown. [ABSTRACT FROM AUTHOR]