What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Himan Shahabi focuses on Artificial intelligence, Receiver operating characteristic, Landslide, Topographic Wetness Index and Flood myth.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Computation.
His Receiver operating characteristic research is included under the broader classification of Statistics.
He combines subjects such as Random forest, Data mining, Spatial database and Pattern recognition with his study of Landslide.
His Topographic Wetness Index research integrates issues from Evolutionary algorithm, Normalized Difference Vegetation Index and Decision tree.
His work in Flood myth addresses subjects such as Stream power, which are connected to disciplines such as Floodplain.
His most cited work include:
- A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. (213 citations)
- Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models (182 citations)
- A novel hybrid artificial intelligence approach for flood susceptibility assessment (175 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Landslide, Receiver operating characteristic, Artificial intelligence, Support vector machine and Normalized Difference Vegetation Index.
The Landslide study combines topics in areas such as Cartography, Bivariate analysis, Statistics and Random forest.
He works mostly in the field of Receiver operating characteristic, limiting it down to concerns involving Mean squared error and, occasionally, Ensemble forecasting and Pruning.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
His research investigates the connection with Support vector machine and areas like Data mining which intersect with concerns in Geographic information system.
Himan Shahabi usually deals with Normalized Difference Vegetation Index and limits it to topics linked to Topographic Wetness Index and Flood myth, Stream power and Decision tree.
He most often published in these fields:
- Landslide (38.28%)
- Receiver operating characteristic (22.66%)
- Artificial intelligence (21.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Support vector machine (19.53%)
- Ensemble forecasting (10.94%)
- Random forest (13.28%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Support vector machine, Ensemble forecasting, Random forest, Landslide and Statistics.
His study in Support vector machine is interdisciplinary in nature, drawing from both Artificial neural network and Receiver operating characteristic.
In his study, which falls under the umbrella issue of Receiver operating characteristic, Decision tree is strongly linked to Topographic Wetness Index.
His Ensemble forecasting study combines topics from a wide range of disciplines, such as Overfitting, Elevation, Classifier, Groundwater and Mean squared error.
His studies deal with areas such as Cartography, Logistic model tree, Remote sensing and Normalized Difference Vegetation Index as well as Landslide.
His Statistics research incorporates themes from Tree, Naive Bayes classifier and Information gain ratio.
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)
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