Salim Heddam

6.5k total citations · 3 hit papers
175 papers, 4.4k citations indexed

About

Salim Heddam is a scholar working on Environmental Engineering, Water Science and Technology and Artificial Intelligence. According to data from OpenAlex, Salim Heddam has authored 175 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Environmental Engineering, 82 papers in Water Science and Technology and 55 papers in Artificial Intelligence. Recurrent topics in Salim Heddam's work include Hydrological Forecasting Using AI (108 papers), Water Quality Monitoring Technologies (45 papers) and Hydrology and Watershed Management Studies (35 papers). Salim Heddam is often cited by papers focused on Hydrological Forecasting Using AI (108 papers), Water Quality Monitoring Technologies (45 papers) and Hydrology and Watershed Management Studies (35 papers). Salim Heddam collaborates with scholars based in Algeria, Iran and China. Salim Heddam's co-authors include Özgür Kişi, Rana Muhammad Adnan, Senlin Zhu, Sungwon Kim, Mohammad Zounemat‐Kermani, Behrooz Keshtegar, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Meysam Alizamir, Alban Kuriqi and Reham R. Mostafa and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Cleaner Production.

In The Last Decade

Salim Heddam

166 papers receiving 4.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Application of Water Quality Indices, Machine Learning Approaches, and GIS to Identify Groundwater Quality for Irrigation Purposes: A Case Study of Sahara Aquifer, Doucen Plain, Algeria

2023 105 citations Salim Heddam et al. Water profile →
An interpretable XGBoost-SHAP machine learning model for reliable prediction of mechanical properties in waste foundry sand-based eco-friendly concrete

2025 27 citations Meysam Alizamir, Mo Wang et al. profile →
Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms

2025 26 citations Khabat Khosravi, Aitazaz A. Farooque et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Salim Heddam Algeria	39	2.5k	2.1k	1.1k	821	534	175	4.4k
Mohammad Zounemat‐Kermani Iran	42	3.1k 1.2×	2.3k 1.1×	1.8k 1.5×	1.3k 1.6×	900 1.7×	154	5.7k
Sungwon Kim South Korea	34	2.0k 0.8×	1.3k 0.6×	1.2k 1.0×	801 1.0×	622 1.2×	121	3.4k
Roohollah Noori Iran	41	2.0k 0.8×	2.2k 1.0×	874 0.8×	349 0.4×	214 0.4×	103	4.7k
Alban Kuriqi Portugal	44	1.9k 0.8×	2.5k 1.2×	2.4k 2.1×	426 0.5×	673 1.3×	174	6.4k
Bahram Gharabaghi Canada	41	2.6k 1.0×	2.1k 1.0×	1.5k 1.3×	442 0.5×	362 0.7×	266	5.7k
Haitham Abdulmohsin Afan Malaysia	32	2.1k 0.8×	1.9k 0.9×	869 0.8×	404 0.5×	416 0.8×	71	3.5k
Ahmed Elbeltagi Egypt	44	2.6k 1.0×	2.3k 1.1×	2.6k 2.3×	481 0.6×	317 0.6×	222	6.0k
Mohammad Ehteram Iran	34	1.7k 0.7×	1.7k 0.8×	803 0.7×	504 0.6×	514 1.0×	111	3.5k
Ali Danandeh Mehr Türkiye	33	1.8k 0.7×	1.4k 0.7×	1.6k 1.4×	346 0.4×	416 0.8×	115	3.3k
Jalal Shiri Iran	48	3.4k 1.4×	2.5k 1.2×	2.3k 2.0×	1.2k 1.5×	716 1.3×	126	5.8k

Countries citing papers authored by Salim Heddam

Since Specialization

Citations

This map shows the geographic impact of Salim Heddam's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Salim Heddam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Salim Heddam more than expected).

Fields of papers citing papers by Salim Heddam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Salim Heddam. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Salim Heddam. The network helps show where Salim Heddam may publish in the future.

Co-authorship network of co-authors of Salim Heddam

This figure shows the co-authorship network connecting the top 25 collaborators of Salim Heddam. A scholar is included among the top collaborators of Salim Heddam based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Salim Heddam. Salim Heddam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ahmadianfar, Iman, et al.. (2026). Towards intelligent air quality forecasting using integrated machine learning framework with variational mode decomposition and catboost feature selection. Scientific Reports. 16(1). 3714–3714. 1 indexed citations

Khosravi, Khabat, et al.. (2025). River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model. Ecological Informatics. 90. 103191–103191. 1 indexed citations

Adnan, Rana Muhammad, Ahmed A. Ewees, Mo Wang, et al.. (2025). Enhancing BOD5 forecasting accuracy with the ANN-Enhanced Runge Kutta model. Journal of environmental chemical engineering. 13(2). 115430–115430. 9 indexed citations

Khosravi, Khabat, Aitazaz A. Farooque, Seyed Amir Naghibi, et al.. (2024). Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models. Ecological Informatics. 85. 102933–102933. 26 indexed citations

Sa’adi, Zulfaqar, Mohammad Al-Suwaiyan, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, et al.. (2024). Observed and future shifts in climate zone of Borneo based on CMIP6 models. Journal of Environmental Management. 360. 121087–121087. 7 indexed citations

Elbeltagi, Ahmed, et al.. (2024). Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm. Journal of Hydrology Regional Studies. 56. 102043–102043. 11 indexed citations

Hai, Tao, Omer A. Alawi, Raad Z. Homod, et al.. (2024). Data driven insights for parabolic trough solar collectors: Artificial intelligence-based energy and exergy performance analysis. Journal of Cleaner Production. 443. 141069–141069. 12 indexed citations

Vishwakarma, Dinesh Kumar, Krishna Kumar Yadav, Rawshan Ali, et al.. (2024). Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions. Pure and Applied Geophysics. 181(2). 719–747. 29 indexed citations

Ptak, Mariusz, Salim Heddam, Soufiane Haddout, Mariusz Sojka, & Teerachai Amnuaylojaroen. (2024). Long-Term Changes in the Thermal and Ice Regime of the Biebrza River (Northeastern Poland) in the Era of Global Warming. Water. 16(22). 3211–3211. 1 indexed citations

10.

Alizamir, Meysam, et al.. (2024). An efficient data fusion model based on Bayesian model averaging for robust water quality prediction using deep learning strategies. Expert Systems with Applications. 261. 125499–125499. 10 indexed citations

11.

Heddam, Salim, Dinesh Kumar Vishwakarma, Salwan Ali Abed, et al.. (2024). Hybrid river stage forecasting based on machine learning with empirical mode decomposition. Applied Water Science. 14(3). 17 indexed citations

12.

Ameur-Zaimeche, Ouafi, Rabah Kechiched, Salim Heddam, et al.. (2024). Machine learning models to predict rare earth elements distribution in Tethyan phosphate ore deposits: Geochemical and depositional environment implications. Geochemistry. 84(4). 126128–126128. 3 indexed citations

13.

Gupta, Sanjeev, Rawshan Ali, Nadhir Al‐Ansari, et al.. (2024). Sensitivity of daily reference evapotranspiration to weather variables in tropical savanna: a modelling framework based on neural network. Applied Water Science. 14(6). 5 indexed citations

14.

Heddam, Salim, Sungwon Kim, Mariusz Ptak, et al.. (2023). River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT. Ecological Informatics. 78. 102376–102376. 17 indexed citations

15.

Abba, Sani I., Mou Leong Tan, Vahdettin Demir, et al.. (2023). Bio-communal wastewater treatment plant real-time modeling using an intelligent meta-heuristic approach: A sustainable and green ecosystem. Journal of Water Process Engineering. 53. 103731–103731. 7 indexed citations

16.

Ahmed, Kaywan Othman, et al.. (2023). Assessment of Hygiene Practices, Awareness, and Water Consumption Regarding Covid-19 Among Children in a Refugee Camp. Maternal and Child Health Journal. 28(3). 545–556. 2 indexed citations

17.

Heddam, Salim, et al.. (2021). Comparison of the advanced machine learning methods for better prediction accuracy of solar radiation using only temperature data: A case study. International Journal of Energy Research. 46(3). 2709–2736. 10 indexed citations

18.

Heddam, Salim, et al.. (2020). Simulation of Monthly Precipitation in Semnan City Using ANN Artificial Intelligence Model. SHILAP Revista de lepidopterología. 13 indexed citations

19.

Heddam, Salim, et al.. (2019). Comparison performance of artificial neural network based method in estimation of electric conductivity in wet and dry periods: Case study of Gamasiab river, Iran. SHILAP Revista de lepidopterología. 3 indexed citations

20.

Hai, Tao, Isa Ebtehaj, Hossein Bonakdari, et al.. (2019). Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme. Energies. 12(7). 1365–1365. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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