What is he best known for?
The fields of study he is best known for:
- Statistics
- Machine learning
- Artificial intelligence
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.
His most cited work include:
- Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models (182 citations)
- Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) (175 citations)
- Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility (122 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Landslide (36.26%)
- Remote sensing (29.67%)
- Support vector machine (18.68%)
What were the highlights of his more recent work (between 2019-2021)?
- Ensemble forecasting (15.38%)
- Landslide (36.26%)
- Statistics (16.48%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. (83 citations)
- A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers (52 citations)
- 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 (43 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Artificial intelligence
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.
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