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Automatic Fast and Robust Technique to Refine Extracted SIFT Key Points for Remote Sensing Images

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Journal of Civil Engineering and Architecture 2020年第6期14卷 339-350页

作者： Hayder Dibs Shattri Mansor Noordin Ahmad Biswajeet Pradhan nadhir a.al-ansari Hydraulic Structures Department Faculty of Water Resources EngineeringAl-Qasim Green UniversityAl-Qasim 964BabylonIraq Department of Civil Engineering Faculty of EngineeringGeospatial Information Science Research CentreUniversity Putra MalaysiaLevel 6Tower BlockSerdang 43400Darul EhsanSelangorMalaysia National Space Agency Malaysia(ANGKASA) Kementerian SainsTeknologi dan InovasiPusat Angkasa NegaraLot 2233Jalan TuriKg.Sg.LangBanting Selangor 42700Malaysia Department of Civil Environmental and Natural Resources Engineering Lulea University of TechnologyLulea 97187Sweden

The scale-invariant feature transform(SIFT)ability to automatic control points(CPs)extraction is very well known on remote sensing images,however,its result inaccurate and sometimes has incorrect matching from generating a small number of false CPs pairs,their matching has high false *** paper presents a method containing a modification to improve the performance of the SIFT CPs matching by applying sum of absolute difference(SAD)in different manner for the new optical satellite generation called near-equatorial orbit satellite(NEqO)and multi-sensor *** proposed method leads to improving CPs matching with a significantly higher rate of correct *** data in this study were obtained from the RazakSAT satellite covering the Kuala Lumpur-Pekan *** proposed method consists of three parts:(1)applying the SIFT to extract CPs automatically,(2)refining CPs matching by SAD algorithm with empirical threshold,and(3)evaluating the refined CPs scenario by comparing the result of the original SIFT with that of the proposed *** result indicates an accurate and precise performance of the model,which showed the effectiveness and robustness of the proposed approach.

关键词： Automatic extraction of ground control point sum of absolute difference near-equatorial satellite multi-sensor,modified SIFT

Erratum to “Rock Fragmentation Classification Applying Machine Learning Approaches” [Engineering, 15 (2023) 378-395]*

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Engineering(科研) 2025年第1期17卷 1-18页

作者： Samaneh Liaghat nadhir al ansari Environmental and Natural Resources Engineering Department Lule University of Technology Lule Sweden

The nature of rock fragmentation affects the downstream mining processes like loading, hauling, and crushing the blasted rock. Therefore, it is important to evaluate rock fragmentation after blasting for choosing or designing optimal strategies for these processes. However, current techniques of rock fragmentation a.alysis such as sieving, image-based a.alysis, empirical methods or artificial intelligence-based methods entail different practical challenges, for example, excessive processing time, higher costs, applicability issues in underground environments, user-biasness, accuracy issues, etc. A classification model has been developed by utilizing image a.alysis techniques to overcome these challenges. The model was tested on about 7500 videos of load-haul-dump (LHD) buckets with blasted material from Malmberget iron ore mine in Sweden. A Kernel-based support vector machine (SVM) method was utilized to extract frames comprising loaded LHD buckets. Then, the blasted rock in the buckets was classified into five distinct categories using the bagging k-nearest neighbor (KNN) technique. The results showed 99.8% and 89.8% accuracy for kernel-based SVM and bagging KNN classifiers, respectively. The developed framework is efficient in terms of the operation time, cost and practicability for different mines and variate amounts of rock masses.

关键词： Rock Blasting Load Dump Haul Image a.alysis Artificial Intelligence Mine Productivity

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 1-41页

作者： Baydaa Abdul Kareem Salah L.Zubaidi nadhir al-ansari Yousif Raad Muhsen Department of Civil Engineering University of MaysanMaysan57000Iraq Department of Civil Engineering Wasit UniversityWasit52001Iraq College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Department of Civil Environmental and Natural Resources Engineering Lulea University of TechnologyLulea97187Sweden Department of Computer Science Faculty of Computer Science and Information TechnologyUniversiti Putra MalaysiaSerdang43400Malaysia

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater *** machine learning(ML)approaches have been enhanced to improve streamflow *** techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone *** researchers have also emphasised using hybrid models to improve forecast ***,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance *** study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML ***’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML *** study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,***,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

关键词： Univariate streamflow machine learning hybrid model data pre-processing performance metrics

Evaluation and modification of some empirical and semi-empirical approaches for prediction of area-storage capacity curves in reservoirs of dams

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International Journal of Sediment Research 2017年第1期32卷 127-135页

作者： Issa E.Issa nadhir al-ansari Govand Sherwany Sven Knutsson Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology Department of Dams and Water Resources Engineering University of Mosul

The storage capacity of reservoirs is gradually reduced due to sediment accumulation that causes changes in the area-storage capacity（ASC） *** these curves and predicting their future change is an important issue for planners,designers and operators of *** empirical and semiempirical approaches have been suggested for establishing and predicting the future changes for these *** this study four empirical and semi-empirical methods were evaluated and three of them were modified to be used for the prediction of changes in the ASC curves due to sedimentation,based on the existing sedimentation survey data for 11 reservoirs in the *** evaluation,these approaches were reviewed and used to determine sedimentation depth and establishing the ASC curves for the Mosul dam reservoir（MDR）,which is the biggest hydraulic structure on the River Tigris in northern *** started operating in 1986 with a storage capacity of 11.11 km;and a water surface area 380 km2 at normal operation stage（330 m a.s.l.）.The results obtained from these methods were evaluated using observed bathymetric survey data that had been collected in 2011 after 25 years of the operation of the *** evaluation results showed three methods had presented more accurate results for estimating water depth or sedimentation depth at dam site with percentage error about 1.06-3.30%.Whilst for establishing ASC curves,one method presented good agreement result with survey ***,ASC and sedimentation depths at dam site of MDR for periods 50,75,100 and 125 years were estimated using the modified approaches and the area reduction *** results of the modified methods provided reasonable agreement when compared with the area reduction method proposed by the *** of Reclamation and the agreement became better with an increase in time period.

关键词： Area-capacity curves Reservoir sedimentation Area reduction method Mosul dam

Can We Restore the Marches in Iraq (Garden of Eden)?

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Engineering（科研） 2020年第7期12卷 466-486页

作者： nadhir al-ansari Department of Civil Environmental and Natural Resources Engineering Lulea University of Technology Lulea Sweden

Iraqi marshes are located within the southern part of Iraq where the Tigris and Euphrates Rivers join. There are three main marshes (Hammar Central and Hawizeh). They used to cover an area 15,000 - 20,000 square kilometers. The government of Iraq started to dry the marshes since 1990 for military reasons. Oil companies started to work in that area, and they occupied about 25% of that area. After 2003, the government changed, and they started to restore the remainder 75% of marshes. To achieve this goal, they require about 13 billion cubic kilometers of water (BCM). The problem was the scarcity of water due to the building of dams in riparian countries and climate change. It is believed that if the government follows a prudent water resources strategy, then it will be possible to restore the marshes.

关键词： Mesopotamia Marsh Garden of Eden Iraq

Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference Systemin Estimation of Compression Coefficient of Plastic Clay Soil

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Computer Modeling in Engineering & Sciences 2022年第1期130卷 149-166页

作者： Manh Duc Nguyen Ha NguyenHai nadhir al-ansari MahdisAmiri Hai-Bang Ly Indra Prakash Binh Thai Pham University of Transport and Communications Hanoi100000Vietnam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran University of Transport Technology Hanoi100000Vietnam DDG(R)Geological Survey of India Gandhinagar382010India

One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the *** this study,the main purpose is to develop a novel hybrid Machine Learning(ML)model(ANFIS-DE),which used Differential Evolution(DE)algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System(ANFIS),for estimating soil Compression coefficient(Cc)from other geotechnical parameters namelyWater Content,Void Ratio,SpecificGravity,Liquid Limit,Plastic Limit,Clay content and Depth of Soil *** of the predictive capability of the novel model was carried out using statistical indices:Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Correlation Coefficient(R).In addition,two popular ML models namely Reduced Error Pruning Trees(REPTree)and Decision Stump(Dstump)were used for *** showed that the performance of the novel model ANFIS-DE is the best(R=0.825,MAE=0.064 and RMSE=0.094)in comparison to other models such as REPTree(R=0.7802,MAE=0.068 and RMSE=0.0988)andDstump(R=0.7325,MAE=0.0785 and RMSE=0.1036).Therefore,the ANFIS-DE model can be used as a promising tool for the correct and quick estimation of the soil Cc,which can be employed in the design and construction of civil engineering structures.

关键词： Compression coefficient differential evolution adaptive-network-based fuzzy inference system machine learning vietnam

Hydro-Politics of the Tigris and Euphrates Basins

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Engineering（科研） 2016年第3期8卷 140-172页

作者： nadhir al-ansari Luleå University of Technology Lule&aring Sweden

The supply of fresh water is essential to life, socioeconomic development, and political stability in Middle East. Turkey, Syria and Iraq are the main riparian countries in the Tigris-Euphrates basin. Turkey is the riparian hegemon for a long time due to its structural power and dominant river position. Iraq and Syria are the lower countries in the basin and for this reason they always like to ensure the quantity of water required to satisfy their requirements. The conflict between riparian countries is due to several factors. These are: population growth rate and food security, energy security, economic and technological development, political fragmentation, international water laws, water and management availability and public awareness. There have been a number of attempts to find a common ground on water issues between the main three riparian countries since 1920 but no agreement signed yet. Present situation is very bad in Syria and Iraq where thousands of people have no access to water and farmers are leaving their land because of draught. Such situation will definitely raise tension and might lead to war. To avoid this, a mediator that has the capability to bring all countries concerned to the negotiation table is required. In addition, Syria and Iraq are to adopt prudent strategic plan based on resources development theory to ensure good water management and minimum water loses.

关键词： Tigris Euphrates Hydro Politics Iraq Turkey Syria

Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in algeria

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Computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil nadhir al-ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and a.alyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was a.alyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial algorithms and Architecture Environment

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Engineering（科研） 2023年第4期15卷 220-233页

作者： Hayder Dibs nadhir al-ansari Department of Water Resources Management Engineering Faculty of Engineering Al-Qasim Green University Babylon Iraq Department of Civil Environmental and Natural Resources Engineering Lulea University of Technology Lulea Sweden

The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.

关键词： World View-3 Satellite Image Sketch Up Digital Surface Model 3D Buildings