Iraq is one of the Middle East and North African countries (MENA region). The country is currently facing a serious water shortage problem. This problem is expected to be more severe in the future where the supply is predicted to be 43 and 17.61 Billion Cubic Meters (BCM) in 2015 and 2025 respectively while current demand is estimated to be between 66.8 and 77 BCM. It has been estimated that the Tigris and Euphrates river discharges will continue to decrease with time, and they will be completely dry by 2040. Serious, prudent and quick measures need to be taken to overcome this problem. The government should take measures to have a strategic water management vision, including regional cooperation and coordination, research and development, improving agriculture and sanitation sector as well as public awareness program. These measures are required in order to address the following topics: Strategic Water Management Vision, Regional cooperation and coordination, Irrigation and Agriculture, Water Supply and Sanitation, and Research and Development.

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.

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.