Mohammad Zounemat‐Kermani

Shahid Bahonar University of Kerman — IR

Environmental Science · Physical Sciences

45h-index7.5kcitations179works7.42yr avg
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Research Topics

Hydrological Forecasting Using AI(100), Hydrology and Watershed Management Studies(45), Energy Load and Power Forecasting(32), Water Quality Monitoring Technologies(25), Hydraulic flow and structures(23)

Publications178 total

Multi-boosting and machine learning for soil substrate water content prediction
Soft Computing·2025
Interpretable boosting algorithms for prediction and data imputation of River Sediment
Applied Soft Computing·2025
Ensemble Boosting Methods for Surface Water Quality Modeling: A Review
Archives of Computational Methods in Engineering·2025
Modeling Coagulant Dosage in Water Treatment Plant Using Explainable Deep Learning Model–Based SHapley Additive exPlanations (SHAP)
Water Conservation Science and Engineering·2025· 2 cited
Interpretable ensemble learning for modeling phosphorus dynamics and mitigating eutrophication in riverine ecosystems
Ecohydrology & Hydrobiology·2025
Spatio-temporal assessment of groundwater and agricultural land using remote sensing in a semi-arid region
Environmental Earth Sciences·2025
Improving the prediction of global solar radiation using interpretable boosting algorithms coupled SHAP and LIME analysis: a comparative study
Theoretical and Applied Climatology·2025· 4 cited
Forecasting maximal and minimal air temperatures using explainable machine learning: Shapley additive explanation versus local interpretable model-agnostic explanations
Stochastic Environmental Research and Risk Assessment·2025· 7 cited
Conventional and advanced AI-based models in soil moisture prediction
Physics and Chemistry of the Earth Parts A/B/C·2025· 6 cited
Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling
Scientific Reports·2025· 21 citedOpen Access
Predicting water quality variables using gradient boosting machine: global versus local explainability using SHapley Additive Explanations (SHAP)
Earth Science Informatics·2025· 26 cited
Enhancing BOD5 forecasting accuracy with the ANN-Enhanced Runge Kutta model
Journal of environmental chemical engineering·2025· 16 cited
Improving groundwater quality predictions in semi-arid regions using ensemble learning models
Environmental Science and Pollution Research·2025· 7 cited
Applications of Advanced Optimized Neuro Fuzzy Models for Enhancing Daily Suspended Sediment Load Prediction
Computer Modeling in Engineering & Sciences·2025· 1 citedOpen Access
Interpretable Boosting Algorithms for Prediction and Data Imputation of River Sediment
SSRN Electronic Journal·2025Open Access
Improved random vector functional link network with an enhanced remora optimization algorithm for predicting monthly streamflow
Journal of Hydrology·2024· 37 cited
Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer
International Journal of Computational Intelligence Systems·2024· 7 citedOpen Access
Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds
Atmosphere·2024· 14 citedOpen Access
New Hybrid Optimization Approaches for the Optimal Management of Surface Water Resources Systems
Water Resources Management·2024· 2 cited
Study of biological quality of lake waters based on phycocyanin using tree‐based methodologies
Ecohydrology·2024· 2 cited
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Frequent Co-authors

Özgür Kişi(33), Sungwon Kim(6), Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬(6), Lunche Wang(5), Rana Muhammad Adnan(5), Reham R. Mostafa(5), Meysam Alizamir(5), Amin Mahdavi‐Meymand(5), Taher Rajaee(4), Vahid Nourani(4), Rana Muhammad Adnan Ikram(4), Reinhard Hinkelmann(3), Salim Heddam(3), Shamsuddin Shahid(3), Hongliang Dai(3), Jalal Shiri(3), Abu Reza Md. Towfiqul Islam(3), Tao Hai(2), Tiyasha Tiyasha(2), Khaled Mohamed Khedher(2)