What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Agriculture
Jan Adamowski mostly deals with Wavelet, Artificial neural network, Wavelet transform, Statistics and Mean squared error.
His Wavelet study is focused on Artificial intelligence in general.
The concepts of his Artificial neural network study are interwoven with issues in Lead time, Data mining, Linear regression, Econometrics and Multivariate statistics.
He interconnects Operations research, Regression analysis and Water resources in the investigation of issues within Linear regression.
His Wavelet transform research is multidisciplinary, incorporating perspectives in Autoregressive integrated moving average, Time series, Hydrology, Streamflow and Meteorology.
His Statistics research incorporates themes from Extreme learning machine and Ensemble forecasting.
His most cited work include:
- A wavelet neural network conjunction model for groundwater level forecasting (356 citations)
- Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (328 citations)
- Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (273 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Water resources, Hydrology, Wavelet, Artificial neural network and Climatology.
His research investigates the connection between Water resources and topics such as Environmental planning that intersect with issues in Stakeholder.
His research in Wavelet transform and Discrete wavelet transform are components of Wavelet.
The study incorporates disciplines such as Meteorology and Autoregressive integrated moving average in addition to Wavelet transform.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Mean squared error, Statistics and Linear regression.
His Climatology research incorporates elements of Climate change, Streamflow, Trend analysis and Precipitation.
He most often published in these fields:
- Water resources (13.87%)
- Hydrology (13.87%)
- Wavelet (12.30%)
What were the highlights of his more recent work (between 2018-2021)?
- Environmental planning (8.12%)
- Mean squared error (8.38%)
- Water resources (13.87%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Environmental planning, Mean squared error, Water resources, Groundwater and Climate change.
Jan Adamowski combines subjects such as Artificial neural network, Convolutional neural network, Correlation coefficient and Time series with his study of Mean squared error.
His Artificial neural network study integrates concerns from other disciplines, such as Ensemble forecasting and Linear regression.
His Water resources research is multidisciplinary, relying on both Data-driven, Flood myth, Probabilistic forecasting and Wavelet.
His work deals with themes such as Hydrogeology, Soil science and Water quality, which intersect with Groundwater.
His research in Climate change intersects with topics in Climatology, Precipitation, Physical geography and Vegetation, Normalized Difference Vegetation Index.
Between 2018 and 2021, his most popular works were:
- An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines (183 citations)
- A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods (127 citations)
- Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model (48 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Agriculture
Jan Adamowski mainly focuses on Statistics, Soil science, Flood myth, Streamflow and Multivariate statistics.
His Artificial neural network research extends to Statistics, which is thematically connected.
The various areas that Jan Adamowski examines in his Artificial neural network study include Autoregressive integrated moving average and Bootstrapping.
His Soil science study combines topics from a wide range of disciplines, such as Tillage and Groundwater.
His research integrates issues of Contrast, Moving average, Multivariate adaptive regression splines, Mathematical optimization and Artificial intelligence in his study of Streamflow.
The Multivariate statistics study combines topics in areas such as Discrete wavelet transform, Watershed, Wind speed and Pan evaporation.
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