D-Index & Metrics Best Publications
Zaher Mundher Yaseen

Zaher Mundher Yaseen

Research.com 2022 Rising Star of Science Award Badge

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 37 Citations 4,861 159 World Ranking 2968 National Ranking 1
Rising Stars D-index 46 Citations 6,553 252 World Ranking 375 National Ranking 2

Research.com Recognitions

Awards & Achievements

2022 - Research.com Rising Star of Science Award

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

Zaher Mundher Yaseen mainly investigates Mean squared error, Artificial intelligence, Artificial neural network, Statistics and Support vector machine. The concepts of his Mean squared error study are interwoven with issues in Coefficient of determination, Correlation coefficient and Time series. His research integrates issues of Reliability and Machine learning in his study of Artificial intelligence.

His work in the fields of Feedforward neural network overlaps with other areas such as Empirical modelling. His research in Statistics focuses on subjects like Streamflow, which are connected to Process and Regression analysis. His Support vector machine study incorporates themes from Extreme learning machine, Spline, Multivariate statistics and Regression.

His most cited work include:

  • An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction (235 citations)
  • Artificial intelligence based models for stream-flow forecasting: 2000–2015 (191 citations)
  • Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq (135 citations)

What are the main themes of his work throughout his whole career to date?

Zaher Mundher Yaseen spends much of his time researching Mean squared error, Artificial neural network, Statistics, Artificial intelligence and Support vector machine. His Mean squared error research integrates issues from Correlation coefficient, Coefficient of determination, Extreme learning machine, Algorithm and Multilayer perceptron. Zaher Mundher Yaseen interconnects Structural engineering and Streamflow in the investigation of issues within Artificial neural network.

His work on Regression, Linear regression and Predictive modelling as part of general Statistics research is frequently linked to Scale, thereby connecting diverse disciplines of science. His research ties Machine learning and Artificial intelligence together. The study incorporates disciplines such as Shear strength and Predictability in addition to Support vector machine.

He most often published in these fields:

  • Mean squared error (28.90%)
  • Artificial neural network (18.81%)
  • Statistics (20.64%)

What were the highlights of his more recent work (between 2020-2021)?

  • Mean squared error (28.90%)
  • Algorithm (8.72%)
  • Artificial intelligence (17.43%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Mean squared error, Algorithm, Artificial intelligence, Support vector machine and Statistics. His work deals with themes such as Soil science, Correlation coefficient, Salp swarm algorithm, Range and Metaheuristic optimization algorithms, which intersect with Mean squared error. His Algorithm research is multidisciplinary, relying on both Genetic algorithm, Swarm behaviour and Series.

His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Wavelet and Wavelet transform. His Support vector machine research includes themes of Uncertainty analysis, Artificial neural network, Coefficient of determination, Sensitivity and Principal component analysis. His study in the field of Predictive modelling, Regression and Markov chain Monte Carlo also crosses realms of Filter.

Between 2020 and 2021, his most popular works were:

  • Reliability-based structural design optimization: hybridized conjugate mean value approach (18 citations)
  • The Application of Soft Computing Models and Empirical Formulations for Hydraulic Structure Scouring Depth Simulation: A Comprehensive Review, Assessment and Possible Future Research Direction (16 citations)
  • Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia. (8 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Machine learning

Zaher Mundher Yaseen mostly deals with Artificial intelligence, Support vector machine, Algorithm, Mean squared error and Rainwater harvesting. His Machine learning research extends to Artificial intelligence, which is thematically connected. His Support vector machine study incorporates themes from Artificial neural network, Statistics and Feature selection.

His work in the fields of Artificial neural network, such as Mean absolute percentage error, overlaps with other areas such as Environmental pollution. The various areas that he examines in his Statistics study include Ensemble forecasting, Bay and Overfitting. His study looks at the intersection of Mean squared error and topics like Predictability with Uncertainty analysis.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Zaher Mundher Yaseen;Sadeq Oleiwi Sulaiman;Ravinesh C. Deo;Kwok Wing Chau.
Journal of Hydrology (2019)

277 Citations

Artificial intelligence based models for stream-flow forecasting: 2000–2015

Zaher Mundher Yaseen;Ahmed El-shafie;Ahmed El-shafie;Othman Jaafar;Haitham Abdulmohsin Afan.
Journal of Hydrology (2015)

239 Citations

Experimental and Numerical Analysis for Earth-Fill Dam Seepage

Ahmed Mohammed Sami Al-Janabi;Abdul Halim Ghazali;Yousry Mahmoud Ghazaw;Haitham Abdulmohsin Afan.
Sustainability (2020)

191 Citations

Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

Zaher Mundher Yaseen;Othman Jaafar;Ravinesh C. Deo;Ozgur Kisi.
Journal of Hydrology (2016)

165 Citations

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

Zaher Mundher Yaseen;Ravinesh C. Deo;Ameer Hilal;Abbas M. Abd.
Advances in Engineering Software (2018)

139 Citations

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Tiyasha;Tran Minh Tung;Zaher Mundher Yaseen.
Journal of Hydrology (2020)

132 Citations

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Haitham Abdulmohsin Afan;Ahmed El-Shafie;Zaher Mundher Yaseen;Mohammed Majeed Hameed.
Water Resources Management (2015)

112 Citations

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

Zaher Mundher Yaseen;Zaher Mundher Yaseen;Isa Ebtehaj;Hossein Bonakdari;Ravinesh C. Deo.
Journal of Hydrology (2017)

112 Citations

Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso

Hai Tao;Lamine Diop;Ansoumana Bodian;Koffi Djaman.
Agricultural Water Management (2018)

101 Citations

Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran

M. A. Ghorbani;M. A. Ghorbani;Ravinesh C. Deo;Zaher Mundher Yaseen;Zaher Mundher Yaseen;Mahsa H. Kashani.
Theoretical and Applied Climatology (2018)

101 Citations

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