A 3D density structure of the lithosphere and upper mantle beneath the eastern Mediterranean Sea (EMS) and its adjacent region was constructed based on gravity anomaly inversion constrained by a ...seismic tomography model. Gravity effects of terrain and crust were removed from the observed gravity field (EIGEN-6C4) to obtain the residual mantle gravity anomaly (RMGA). The density distribution of the lithosphere and upper mantle was investigated. The 3D inversion process was constrained by an initial density model projected from the shear-wave velocity model (SL2013sv). The results show some characteristics of the density distribution in the lithosphere and in the upper mantle that could be related to the tectonic importance of the Mediterranean Sea and the surrounding region. A low-density zone dominates the lithosphere beneath the Sea except for the area around the Arabia Shield and North Anatolian Fault belt. A thinner, high-density layer beneath the southwest of the Sea may be related to the older oceanic lithospheric fragments. The high-density anomalies appear at depths below 280 km beneath the Sea and the Turkish Aegean Sea Plate. However, the low-density anomalies appear on the upper mantle under the trenches of the southwestern part of the Mediterranean Sea, the eastern part of the Aegean Sea, the Red Sea, the Black Sea, and the middle of the Arabia shield. The deep structure under the Eratosthenes seamount in the Mediterranean Sea is the source of the intensity and genesis of tectonic activity. Furthermore, the convergence region of two low-density anomaly zones (Africa-Anatolia) may be interpreted as a significant tectonic unit (Eratosthenes seamount) caused by the arrival of the relatively thick and buoyant Eratosthenes block to its present location south of Cyprus in Holocene time based on the density model interpretation beneath the Mediterranean Sea during the Late Cretaceous and early Tertiary period.
•3-D density structure of the lithosphere and upper-mantle beneath the eastern Mediterranean Sea (EMS) and adjacent region is obtained.•Determination thermal and composition structure of the upper- mantle to understand the tectonic evolution under the Mediterranean Sea and the adjacent regions.•A thinner high-density layer appears beneath the southwest of the Mediterranean Sea, and it may be related to the older oceanic lithosphere fragments•The deep structure under the Eratosthenes seamount in the Mediterranean Sea is the source of the intensity and genesis of tectonic activity.•The convergence region of the two low-density anomaly zones (Africa-Anatolia) may be interpreted as a significant tectonic unit (Eratosthenes seamount).
The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training ...algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter ...describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) ...and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP and autoencoder‐MLP models were, respectively, 75 and 90, 74 and 93% in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.
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, NO2, O3, PM2.5 and PM10 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 NO2, PM2.5, and PM10 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 O3 for same period. During the 2nd partial lockdown from June 14 to July 24, NO2 and PM2.5 decreases 20 and 2.5%, respectively. While, there were 525 and 56% increase in O3 and PM10, 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 NO2 tropospheric column extracted from the Sentinel-5P satellite shown the NO2 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.
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•NO2 concentrations reduced by 6, 7, 8 and 20%, respectively in Baghdad during the lockdown.•O3 concentrations increased by 13%, 75%, 225% and 525%, for the same periods.•AQI improved in Baghdad by 13%, compared to the pre-lockdown.•NO2 emissions reduced up to 35 to 40% in Iraq compared to the pre-lockdown.
Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models ...called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.
Iran's Agriculture in the Anthropocene Maghrebi, Mohsen; Noori, Roohollah; Bhattarai, Rabin ...
Earth's future,
September 2020, Volume:
8, Issue:
9
Journal Article
Peer reviewed
Open access
The anthropogenic impacts of development and frequent droughts have limited Iran's water availability. This has major implications for Iran's agricultural sector which is responsible for about 90% of ...water consumption at the national scale. This study investigates if declining water availability impacted agriculture in Iran. Using the Mann‐Kendall and Sen's slope estimator methods, we explored the changes in Iran's agricultural production and area during the 1981–2013 period. Despite decreasing water availability during this period, irrigated agricultural production and area continuously increased. This unsustainable agricultural development, which would have been impossible without the overion of surface and ground water resources, has major long‐term water, food, environmental, and human security implications for Iran.
Plain Language Summary
Given the heavy reliance of the agricultural sector on water availability, it is important to examine if Iran's agriculture has been impacted by water availability changes in recent decades. The investigation of the long‐term impacts of natural water availability changes on agricultural activities in the country during the 1981–2013 period revealed that the agricultural sector in Iran continued to expand regardless of decreasing water availability in the country. This expansion was facilitated by the excessive use of nonrenewable water resources which has significant environmental and socioeconomic implications.
Key Points
Trends in Iran's agricultural production and area did not follow natural water availability changes due to meteorological variability
Iran's agricultural production continuously increased despite water availability reduction during 1981–2013
The unsustainable growth of Iran's agriculture has important water, food, environmental, economic, and human security implications
Water Footprint of Wheat in Iraq Ewaid, Salam Hussein; Abed, Salwan Ali; Al-Ansari, Nadhir
Water (Basel),
03/2019, Volume:
11, Issue:
3
Journal Article
Peer reviewed
Open access
The water footprint (WF) is an indicator of indirect and direct fresh water use. In respect of facilitating decision-making processes, WF gives an excellent perspective on how and where fresh water ...is used in the supply chain. More than 39 million people live in Iraq and, with a growing population, there is a water shortage and a rising demand for food that cannot be met in the future. In this study, the WF of wheat production is estimated for the year 2016–2017 for 15 Iraqi provinces. The WF was calculated using the method of Mekonnen and Hoekstra (2011) and the CROPWAT and CLIMWAT softwares’ crop water requirement option. It was found that the WF in m3/ton was 1876 m3/ton. The 15 provinces showed variations in WFs, which can be ascribed to the difference in climate and production values. The highest wheat WF was found in Nineveh province, followed by Muthanna, Anbar, and Basra. The last three provinces produce little and have a high WF so, in these provinces, wheat can be replaced with crops that need less water and provide more economic benefit. There is an opportunity to reduce the green WF by increasing production from the 4 rain-fed provinces, which will reduce the need for production from the irrigated provinces and, therefore, reduce the use of blue water.
Hydrological soil group is essential to soil information for several fields of modeling and applications. This information can affect suitable environmental, agricultural, and hydrological ...development. Laboratory analysis for soil sampling cannot efficiently provide the needed information because these analyses are commonly costly, time-consuming, and limited in retrieving the temporal and spatial variability. In this context, remote sensing is now solid to offer meaningful spatial data for studying soil characteristics on various spatial scales utilizing the different spectral reflectance. For this study, the integration of Geographic Information System (GIS) remote sensing data and survey data with the Artificial Neural Network (ANN) were used to generate a hydrological soil group map and to infer spatial patterns of soils across complete area converges for Alghadaf Wadi in the Western Desert of Iraq. The generated soil information was tested based on the sand, silt, and clay content. The testing result indicated that the differences between actual and predicted values to determine soil classes are agreed well. Therefore, this method is vital for mapping and monitoring soil texture by providing timely, fast repetitive data and relatively cheap.
Salinity and sodicity have been a major environmental hazard of the past century since more than 25% of the total land and 33% of the irrigated land globally are affected by salinity and sodicity. ...Adverse effects of soil salinity and sodicity include inhibited crop growth, waterlogging issues, groundwater contamination, loss in soil fertility and other associated secondary impacts on dependent ecosystems. Salinity and sodicity also have an enormous impact on food security since a substantial portion of the world’s irrigated land is affected by them. While the intrinsic nature of the soil could cause soil salinity and sodicity, in developing countries, they are also primarily caused by unsustainable irrigation practices, such as using high volumes of fertilizers, irrigating with saline/sodic water and lack of adequate drainage facilities to drain surplus irrigated water. This has also caused irreversible groundwater contamination in many regions. Although several remediation techniques have been developed, comprehensive land reclamation still remains challenging and is often time and resource inefficient. Mitigating the risk of salinity and sodicity while continuing to irrigate the land, for example, by growing salt-resistant crops such as halophytes together with regular crops or creating artificial drainage appears to be the most practical solution as farmers cannot halt irrigation. The purpose of this review is to highlight the global prevalence of salinity and sodicity in irrigated areas, highlight their spatiotemporal variability and causes, document the effects of irrigation induced salinity and sodicity on physicochemical properties of soil and groundwater, and discuss practical, innovative, and feasible practices and solutions to mitigate the salinity and sodicity hazards on soil and groundwater.
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