Ozgur Kisi

Guest Professor

University of Applied Sciences

Germany

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Ozgur Kisi is a Guest Professor at the University of Applied Sciences in Germany. His research areas include agricultural sustainability, climatic variations, and machine learning applications in environmental modeling. Recent articles authored by Professor Kisi explore topics such as saline irrigation impacts on grape yields, precipitation trends in Niedersachsen, and advanced modeling techniques for water quality and solar radiation forecasting.

Articles (20)

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2025

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2023

Collaborators (4)

Alban Kuriqi

Universidade de Aveiro

PORTUGAL

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Sungwon Kim

Dongyang University

SOUTH KOREA

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Il-Moon Chung

University of Science and Technology (UST)

SOUTH KOREA

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Christoph Külls

Professor

Technical University of Applied Sciences Lübeck

GERMANY

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Ozgur Kisi

Research Interests

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Articles (20)

Sustaining Grape Yield and Soil Health Under Saline–Sodic Irrigation Through Amendments and Canal Water Application

Spatial-temporal variation of precipitation in Niedersachsen, Germany, using statistical and graphical trend methods considering climatic drivers

Comparison Analysis of Seepage Through Homogenous Embankment Dams Using Physical, Mathematical and Numerical Models

Forecasting maximal and minimal air temperatures using explainable machine learning: Shapley additive explanation versus local interpretable model-agnostic explanations

Novel Hybrid Approach for River Inflow Modeling: Case Study of the Indus River Basin, Pakistan

Improving the prediction of global solar radiation using interpretable boosting algorithms coupled SHAP and LIME analysis: a comparative study

Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP)

A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting

Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds

Enhancing solar radiation prediction accuracy: A hybrid machine learning approach integrating response surface method and support vector regression

Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

Optimization design of quality monitoring network of Urmia plain using genetic algorithm and vulnerability map

Comparison of improved relevance vector machines for streamflow predictions

Evaluating the effects of reservoir outflow and land-use change on the Zarrineh River basin

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

Investigating Landfill Leachate and Groundwater Quality Prediction Using a Robust Integrated Artificial Intelligence Model: Grey Wolf Metaheuristic Optimization Algorithm and Extreme Learning Machine

Improving Significant Wave Height Prediction Using a Neuro-Fuzzy Approach and Marine Predators Algorithm

Predicting and Optimizing the Influenced Parameters for Culvert Outlet Scouring Utilizing Coupled FLOW 3D-Surrogate Modeling

Application of novel binary optimized machine learning models for monthly streamflow prediction

Collaborators (4)

Alban Kuriqi

Sungwon Kim

Il-Moon Chung

Christoph Külls

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