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Publications (10 of 342) Show all publications
Pham, B. T., Phong, T. V., Nguyen, H. D., Qi, C., Al-Ansari, N., Amini, A., . . . Bui, D. T. (2020). A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water, 12(1), 1-21, Article ID 239.
Open this publication in new window or tab >>A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
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2020 (English)In: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 12, no 1, p. 1-21, article id 239Article in journal (Refereed) Published
Abstract [en]

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020
Keywords
flash flood, kernel logistic regression, radial basis function network, multinomial naïve
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77419 (URN)10.3390/w12010239 (DOI)
Note

Validerad;2020;Nivå 2;2020-01-24 (johcin)

Available from: 2020-01-15 Created: 2020-01-15 Last updated: 2020-01-24Bibliographically approved
Bui, D. T., Shirzadi, A., Amini, A., Shahabi, H., Al-Ansari, N., Hamidi, S., . . . Ghazvinei, P. T. (2020). A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers. Sustainability, 12(3), 1-24, Article ID 1063.
Open this publication in new window or tab >>A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers
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2020 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 12, no 3, p. 1-24, article id 1063Article in journal (Refereed) Published
Abstract [en]

Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020
Keywords
scour depth, complex piers, pile cap, machine learning algorithms, ensemble models
National Category
Geotechnical Engineering
Research subject
Soil Mechanics; Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77620 (URN)10.3390/su12031063 (DOI)
Note

Validerad;2020;Nivå 2;2020-02-04 (johcin)

Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-04Bibliographically approved
Mohammad, M. E., Al-Ansari, N., Knutsson, S. & Laue, J. (2020). A numerical study of pumping effects on flow velocity distributions in Mosul Dam reservoir using the HEC‐RAS model. Lakes & Reservoirs: Research and Management
Open this publication in new window or tab >>A numerical study of pumping effects on flow velocity distributions in Mosul Dam reservoir using the HEC‐RAS model
2020 (English)In: Lakes & Reservoirs: Research and Management, ISSN 1320-5331, E-ISSN 1440-1770Article in journal (Refereed) Epub ahead of print
Abstract [en]

Water flow direction and velocity affect and controls erosion, transport and deposi- tion of sediment in rivers, reservoirs and different hydraulic structures. One of the main structures affected is pumping stations within the dams wherein the velocity distribution near the station intake is disturbed. The two-dimensional (2-D) HEC-RAS 5.01 model was utilized to study, analyse and evaluate the effects of pumping rates and flow depth on the flow velocity distribution, flow stream power and their effects in the Mosul Dam reservoir. The pumping station was considered as a case study. The station is suffering from sediment accumulation around, and in, its intake and suction pipes. The main inflow sources to the reservoir are the Tigris River and run-off from the valleys within its basin. The reservoir was divided into two parts for the present study, including the upper part near the pumping station (analysed as a two-dimen- sional zone), while the lower part was analysed as a one-dimensional flow to reduce the simulation period computation time (1986–2011). Different operation plans (i.e. pumping rate and water depth) were considered. The results of the depth-averaged velocity model indicated that when the pumping station was working at a range from the designed full capacity (100% to 25% of its full capacity), the maximum flow ve- locity increased from 75 to 4 times the normal velocity when there is no pumping dependent on pumping rate and flow depth. For the same operation plans, the flow stream power varied from around zero values to 400 times at full pumping capacity and low flow depth. For sediment routing along the reservoir, the considered statisti- cal criteria indicated the model performance in estimating the total sediment load deposition and invert bed level is much better than in the case of erosion and deposition areas for different considered bed sections of the reservoir.

Place, publisher, year, edition, pages
USA: John Wiley & Sons, 2020
Keywords
pumping station, sediment concentration, stream power, velocity distribution
National Category
Geotechnical Engineering
Research subject
Soil Mechanics; Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77542 (URN)10.1111/lre.12306 (DOI)
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-02-04
Abdullah, T., Ali, S., Al-Ansari, N. & Knutsson, S. (2020). Assessment of groundwater vulnerability to pollution using two different vulnerability models in Halabja-Saidsadiq Basin, Iraq. Groundwater for Sustainable Development, 10, Article ID 100276.
Open this publication in new window or tab >>Assessment of groundwater vulnerability to pollution using two different vulnerability models in Halabja-Saidsadiq Basin, Iraq
2020 (English)In: Groundwater for Sustainable Development, ISSN 2352-801X, Vol. 10, article id 100276Article in journal (Refereed) Published
Abstract [en]

Groundwater aquifer in Halabja-Saidsadiq Basin considered as one of the most important aquifers in terms of water supplying in Kurdistan Region, NE of Iraq. The growing of economics, irrigation and agricultural activities inside the basin makes it of the main essentials to the region. Therefore, pollution of groundwater is of specific worry as groundwater resources are the principal source of water for drinking, agriculture, irrigation and industrial activities. Thus, the best and practical arrangement is to keep the pollution of groundwater through. The current study aims to evaluate of the vulnerability of groundwater aquifers of the study area. Two models were applied, to be specific VLDA and COP to develop maps of groundwater vulnerability for contamination. The VLDA model classified the area into four classes of vulnerability: low, moderate, high and very high with coverage area of (2%,44%,53% and 1%), respectively. While four vulnerability classes were accomplished dependent on COP model including very low, low, moderate and high vulnerability classes with coverage areas of (1%, 37%, 2% and 60%) respectively. To confirm the suitability of each map for assessment of groundwater vulnerability in the area, it required to be validated of the theoretical sympathetic of current hydrogeological conditions. In this study, groundwater age evaluated utilizing tritium isotopes investigation and applied it to validate the vulnerability results. Based on this validation, the outcome exhibits that the vulnerability classes acquired utilizing VLDA model are more predictable contrasted with the COP model.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Vulnerability, VLDA, COP, Halabja-Saidsadiq basin (HSB), Iraq
National Category
Engineering and Technology Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-76166 (URN)10.1016/j.gsd.2019.100276 (DOI)2-s2.0-85072644933 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-10-01 (johcin)

Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-12-18Bibliographically approved
Ewaid, S. H., Abed, S. A. & Al-Ansari, N. (2020). Assessment of Main Cereal Crop Trade Impacts on Water and Land Security in Iraq. Agronomy, 10(1), 1-14, Article ID 98.
Open this publication in new window or tab >>Assessment of Main Cereal Crop Trade Impacts on Water and Land Security in Iraq
2020 (English)In: Agronomy, E-ISSN 2073-4395, Vol. 10, no 1, p. 1-14, article id 98Article in journal (Refereed) Published
Abstract [en]

Growing populations, socio-economic development, the pollution of rivers, and the withdrawal of fresh water are all signs of increasing water scarcity, and with 85% of global use, agriculture is the biggest freshwater user. The water footprint (WF) and virtual water (VW) are concepts used recently for freshwater resources assessment. The WF reflects how much, when and where the water was used whereas VW reveals the volume of water embedded in goods when traded. The first goal of this research is to determine the WF per ton and the WF of production (Mm3/yr) of wheat, barley, rice, and maize in Iraq. The second goal is estimating the quantities of the 4 main cereal crops imported into Iraq and assessing the impact on reducing WF and land savings for 10 years from 2007 to 2016. The results showed that the WF per ton was 1736, 1769, 3694, 2238 m3/ton and the WF of production was 5271, 1475, 997, 820 Mm3/yr for wheat, barley, rice, and maize, respectively. The median total VW imported was 4408 Mm3/yr, the largest volume was 3478 Mm3/yr from wheat, and Iraq saved about 2676 Mm3 of irrigated water and 1,239,539 M ha of land by importing crops every year during 2007–2016. The study revealed the significance of better irrigation management methods to decrease the WF through a selection of crops that need less water and cultivation in rain-fed areas, as well as the use of cereal import to conserve scarce water resources, which is crucial both in terms of water resource management and preservation of the environment. The results of this research could be used as a guideline for better water management practices in Iraq and can provide helpful data for both stakeholders and policymakers.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020
Keywords
cereal crops, virtual water trade, footprint, Iraq
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77331 (URN)10.3390/agronomy10010098 (DOI)
Note

Validerad;2020;Nivå 2;2020-01-24 (johcin)

Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-01-24Bibliographically approved
Al-Jabban, W., Laue, J., Knutsson, S. & Al-Ansari, N. (2020). Briefing: Common laboratory procedures to prepare and cure stabilised soil specimens: a short review. Geotechnical Research, 7(1), 1-8
Open this publication in new window or tab >>Briefing: Common laboratory procedures to prepare and cure stabilised soil specimens: a short review
2020 (English)In: Geotechnical Research, ISSN 2052-6156, Vol. 7, no 1, p. 1-8Article in journal (Refereed) Epub ahead of print
Abstract [en]

Soil stabilisation is used extensively to improve the physical and mechanical properties of soils to achieve the desired strength and durability properties. During the design process, laboratory investigation is conducted firstly to obtain an enhancement in soil strength and stiffness, in addition to the type and amount of binder required. The methods of preparing and curing specimens of soil–binder mixtures directly influence the properties of the stabilised soils. The most common laboratory protocols used for preparing and curing the specimens of stabilised soil are presented in this short review. The review focuses on several aspects such as homogenisation of the natural soil, mixing type and duration, mould type, moulding techniques and curing time and condition. This review can assist various construction projects that deal with soil improvement to choose an appropriate method for preparing and curing a soil–binder mixture to simulate the field conditions as much as possible and obtain uniform soil–binder mixtures.

Place, publisher, year, edition, pages
UK: ICE Publishing is a division of Thomas Telford Ltd, 2020
Keywords
soil specimens, Binder, stabilised soil, laboratory procedures
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77348 (URN)10.1680/jgere.19.00035 (DOI)
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-24
Bui, D. T., Asl, D. T., Ghanavati, E., Al-Ansari, N., Khezri, S., Chapi, K., . . . Pham, B. T. (2020). Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin. Sustainability, 12(338)
Open this publication in new window or tab >>Effects of Inter-Basin Water Transfer on Water Flow Condition of Destination Basin
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2020 (English)In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 12, no 338Article in journal (Refereed) Published
Abstract [en]

In recent years, the intensification of drought and unsustainable management and use of water resources have caused a significant decline in the water level of the Urmia Lake in the northwest of Iran. This condition has affected the lake, approaching an irreversible point such that many projects have been implemented and are being implemented to save the natural condition of the Urmia Lake, among which the inter-basin water transfer (IBWT) project from the Zab River to the lake could be considered an important project. The main aim of this research is the evaluation of the IBWT project effects on the Gadar destination basin. Simulations of the geometrical properties of the river, including the bed and flow, have been performed, and the land cover and flood map were overlapped in order to specify the areas prone to flood after implementing the IBWT project. The results showed that with the implementation of this project, the discharge of the Gadar River was approximately tripled and the water level of the river rose 1 m above the average. In April, May, and June, about 952.92, 1458.36, and 731.43 ha of land adjacent to the river (floodplain) will be inundated by flood, respectively. Results also indicated that UNESCO’s criteria No. 3 (“a comprehensive environmental impact assessment must indicate that the project will not substantially degrade the environmental quality within the area of origin or the area of delivery”) and No. 5 (“the net benefits from the transfer must be shared equitably between the area of origin and the area of water delivery”) have been violated by implementing this project in the study area. The findings could help the local government and other decision-makers to better understand the effects of the IBWT projects on the physical and hydrodynamic processes of the Gadar River as a destination basin.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020
Keywords
inter-basin water transfer project, flood inundation, destination basin, Gadar river
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77272 (URN)10.3390/su12010338 (DOI)
Note

Validerad;2020;Nivå 2;2020-01-09 (johcin)

Available from: 2020-01-01 Created: 2020-01-01 Last updated: 2020-01-09Bibliographically approved
Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J. J., . . . Ahmad, A. (2020). Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing, 12(2), 1-30, Article ID 266.
Open this publication in new window or tab >>Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
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2020 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 2, p. 1-30, article id 266Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI, 2020
Keywords
flood, machine learning, remote sensing data, goodness-of-fit, overfitting, Haraz, Iran
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77370 (URN)10.3390/rs12020266 (DOI)
Note

Validerad;2020;Nivå 2;2020-01-24 (johcin)

Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-01-24Bibliographically approved
Adamo, N., Al-Ansari, N. & Sissakian, V. K. (2020). Global Climate Change Impacts on Tigris-Euphrates Rivers Basins. Journal of Earth Sciences and Geotechnical Engineering, 10(1), 49-98
Open this publication in new window or tab >>Global Climate Change Impacts on Tigris-Euphrates Rivers Basins
2020 (English)In: Journal of Earth Sciences and Geotechnical Engineering, ISSN 1792-9040, E-ISSN 1792-9660, Vol. 10, no 1, p. 49-98Article in journal (Refereed) Published
Abstract [en]

Climate change is affecting the hydrological cycle all over the World. The effect on arid and semi-arid regions is relatively more. The Middle East and North Africa region is one of the biggest hyper-arid, semi-arid and arid zones in the world where the long-term average precipitation does not exceed 166mm per year. The Tigris and Euphrates basins are located within the northern part of the Middle East. Future projections  indicate  the  considerable  reduction  in  water  resources  as  a  result  of drought and population growth. North Atlantic Oscillation (NAO) is responsible for the  change  in  climate  over  the  Tigris  and  Euphrates  basins.  This  is  causing  a decrease in rainfall and a consequence decrease in the flow of the rivers. In addition, the  temperature  is  increasing.  All  these  variables  are  causing  sea  level  rise, increasing dust storms and deletion of groundwater resources. It is believed that quick actions are required to minimize the effect of climate change. This includes prudent water resources planning and good regional cooperation.

Place, publisher, year, edition, pages
UK: Scientific Press International Limited, 2020
Keywords
Tigris, Euphrates, Climate change, Iraq
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77199 (URN)
Note

Validerad;2020;Nivå 1;2019-12-18 (johcin)

Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-18Bibliographically approved
Hai, T., Sharafati, A., Mohammed, A., Salih, S. Q., Deo, R. C., Al-Ansari, N. & Yaseen, Z. M. (2020). Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model. IEEE Access, 8, 12026-12042
Open this publication in new window or tab >>Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 12026-12042Article in journal (Refereed) Published
Abstract [en]

Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information’s are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model’s estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm −2 compared to 4.24 and 3.24 Wm −2 (MLR) and 8.33 and 5.37 Wm −2 (ARIMA).

Place, publisher, year, edition, pages
USA: IEEE, 2020
Keywords
Energy feasibility studies, extreme learning machine, solar energy estimation, multivariate
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
urn:nbn:se:ltu:diva-77489 (URN)10.1109/ACCESS.2020.2965303 (DOI)
Note

Validerad;2020;Nivå 2;2020-01-23 (johcin)

Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-01-23Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-6790-2653

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