Machine Learning and AI applications

Machine Learning, AI, and Related Applications

Recent Publications (2023-2025)

  1. Islam et al. (2025): “A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches.” Remote Sensing 17(3):524.
  2. Lukas et al. (2024): “Predicting reservoir sedimentation using multilayer perceptron – Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia.” Journal of Environmental Management 359:121018.
  3. Lukas et al. (2023): “Prediction of Future Land Use/Land Cover Changes Using A Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia.” Remote Sensing 15(4):1148.

2020-2022 Publications

  1. Kayhomayoon et al. (2022): “How does a combination of numerical modeling, clustering, artificial intelligence, and evolutionary algorithms perform to predict regional groundwater levels?” Computers and Electronics in Agriculture 203:107482.
  2. Abiy et al. (2022): “Multilayer Feedforward Artificial Neural Network Model to Forecast Florida Bay Salinity with Climate Change.” Water 14:3495.
  3. Ghasemian et al. (2022): “Application of Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountain Area.” Frontiers in Environmental Science 657.
  4. Khosravi et al. (2022): “Intelligent flow Discharge computation in a rectangular channel with free overfall condition.” Neural Computing and Applications 1-16.
  5. Tao et al. (2021): “Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions.” Engineering Applications of Computational Fluid Mechanics 15(1):1585-1612.
  6. Panahi et al. (2021): “Cumulative Infiltration and Infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran.” Journal of Hydrology: Regional Studies.
  7. Esmali Ouri et al. (2020): “Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques.” Land 9:368.
  8. Melesse et al. (2020): “River Water Salinity Prediction Using Hybrid Machine Learning Models.” Water 12:2951.
  9. Gebreslassie et al. (2020): “Linear spectral unmixing algorithm for modeling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia.” International Journal of Sediment Research 35(1):79-90.

2015-2019 Publications

  1. Lee et al. (2019): “SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment.” Applied Sciences 9:3495.
  2. Nohani et al. (2019): “Flood spatial modeling in northern Iran using remote sensing and GIS: A comparison between evidential belief functions and their ensemble with multivariate logistic regression models.” Remote Sensing 11:1589.
  3. Khosravi et al. (2019): “Flood susceptibility mapping at Ningdu Catchment, China using Bivariate and Data Mining Techniques.” In: Extreme Hydrology and Climate Variability.
  4. Birhanu et al. (2016): “Bias Correction and Characterization of Climate Forecast System Reanalysis Daily Precipitation in Ethiopia Using Fuzzy Overlay.” Meteorological Applications.
  5. Rahmati et al. (2015): “Application of GIS based data driven random forest and maximum entropy models for groundwater potential mapping.” CATENA 137:360-372.

2002-2014 Publications

  1. Chebud et al. (2012): “Water quality monitoring using a remote sensing and artificial neural network.” Water, Air, & Soil Pollution 223(8):4875-4887.
  2. Melesse et al. (2011): “Sediment Load prediction in Large Rivers: ANN Approach.” Agricultural Water Management 98:855-86.
  3. Melesse et al. (2008): “Modeling Coastal Eutrophication at Florida Bay using neural networks.” Journal of Coastal Research 24:190-196.
  4. Wang et al. (2006): “Development of a Multivariate Regression Model for Soil Nitrate-Nitrogen Content Prediction.” Journal of Spatial Hydrology 6(2):38-56.
  5. Melesse & Hanley (2005): “Artificial Neural Network Application for Multi-Ecosystem Carbon Flux Simulation.” Ecological Modeling 189:305-314.
  6. Melesse & Hanley (2005): “Energy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural Network.” American Journal of Applied Sciences 2(2):491-495.
  7. Melesse & Jordan (2002): “A Comparison of Fuzzy vs. Augmented-ISODATA Classification Algorithm for Cloud and Cloud-Shadow Discrimination in Landsat Imagery.” Photogrammetric Engineering and Remote Sensing 68(9):905-911.