2022 - Research.com Rising Star of Science Award
Baharin Bin Ahmad focuses on Statistics, Receiver operating characteristic, Topographic Wetness Index, Normalized Difference Vegetation Index and Landslide. Baharin Bin Ahmad has researched Statistics in several fields, including Ensemble forecasting and Flood myth. His Receiver operating characteristic research integrates issues from Logistic model tree and Support vector machine, Artificial intelligence.
Baharin Bin Ahmad combines subjects such as Standard error, Feature selection and Geographic information system with his study of Normalized Difference Vegetation Index. His studies deal with areas such as Land cover and Cartography as well as Landslide. His Land cover research is multidisciplinary, incorporating elements of Remote sensing, Fuzzy logic and Statistical model.
Landslide, Remote sensing, Support vector machine, Land cover and Statistics are his primary areas of study. His Landslide study combines topics from a wide range of disciplines, such as Cartography, Logistic regression, Normalized Difference Vegetation Index and Receiver operating characteristic. His Remote sensing study combines topics in areas such as Ground truth, Snowmelt and Satellite data.
Artificial intelligence and Machine learning are the areas that his Support vector machine study falls under. His studies in Statistics integrate themes in fields like Ensemble forecasting, Random forest, Flood myth and Topographic Wetness Index. His research in Mean squared error focuses on subjects like Information gain ratio, which are connected to Soft computing.
His main research concerns Ensemble forecasting, Landslide, Statistics, Remote sensing and Hydrology. His research integrates issues of Mean squared error, Elevation, Classifier and Data mining in his study of Ensemble forecasting. The Mean squared error study combines topics in areas such as Correlation coefficient and Information gain ratio.
His Landslide research includes elements of Goodness of fit, Logistic regression, Logistic model tree and Random forest. His study in Statistics is interdisciplinary in nature, drawing from both Tree and Naive Bayes classifier. In his work, Consistency is strongly intertwined with Normalized Difference Vegetation Index, which is a subfield of Remote sensing.
Baharin Bin Ahmad mainly investigates Ensemble forecasting, Classifier, Data mining, Flood myth and Mean squared error. His Ensemble forecasting research incorporates themes from Elevation, Stream power, Decision tree, Groundwater and Ensemble learning. His Classifier research incorporates elements of Watershed, Overfitting, k-nearest neighbors algorithm, Goodness of fit and Flooding.
His Flood myth research is multidisciplinary, incorporating perspectives in Tree, Random forest and Statistics. His Mean squared error research includes themes of Artificial neural network, Correlation coefficient and Support vector machine.
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.
Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)
Haoyuan Hong;Haoyuan Hong;Junzhi Liu;Junzhi Liu;Dieu Tien Bui;Biswajeet Pradhan;Biswajeet Pradhan.
Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models
Himan Shahabi;Saeed Khezri;Baharin Bin Ahmad;Mazlan Hashim.
GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
Wei Chen;Hui Li;Enke Hou;Shengquan Wang.
Science of The Total Environment (2018)
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
Wei Chen;Ataollah Shirzadi;Himan Shahabi;Baharin Bin Ahmad.
Geomatics, Natural Hazards and Risk (2017)
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.
Wei Chen;Yang Li;Weifeng Xue;Himan Shahabi.
Science of The Total Environment (2020)
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
Wei Chen;Mahdi Panahi;Paraskevas Tsangaratos;Himan Shahabi.
Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
Wei Chen;Wei Chen;Haoyuan Hong;Haoyuan Hong;Shaojun Li;Himan Shahabi.
Journal of Hydrology (2019)
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
Himan Shahabi;Ataollah Shirzadi;Kayvan Ghaderi;Ebrahim Omidvar.
Remote Sensing (2020)
New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling
Dieu Tien Bui;Khabat Khosravi;Shaojun Li;Himan Shahabi.
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
Qingfeng He;Himan Shahabi;Ataollah Shirzadi;Shaojun Li.
Science of The Total Environment (2019)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: