Výsledky vyhledávání - "Rida Al-Adamat"

Akademický článek

Spatial mapping of water spring potential using four data mining models

Autor: Abdel Rahman Al-Shabeeb, Ibraheem Hamdan, A'kif Al-Fugara, Rida Al-Adamat, Mohammed Alrawashdeh

Publikováno v: Water Supply, Vol 23, Iss 5, Pp 1743-1759 (2023)

Population growth and overexploitation of water resources pose ongoing pressure on groundwater resources. This study compares the capability of four data mining methods, namely, boosted regression tree (BRT), random forest (RF), multivariate adaptive

Externí odkaz: https://doaj.org/article/f835396d41284c619dcd481948b7e7ae

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Akademický článek

Spatial mapping of landslide susceptibility in Jerash governorate of Jordan using genetic algorithm-based wrapper feature selection and bagging-based ensemble model

Autor: Abdel Rahman Al-Shabeeb, A’kif Al-Fugara, Khaled Mohamed Khedher, Ali Nouh Mabdeh, Rida Al-Adamat

Publikováno v: Geomatics, Natural Hazards & Risk, Vol 13, Iss 1, Pp 2252-2282 (2022)

This study employs five genetic algorithm (GA)-based machine learning (ML) models, namely the Decision Tree (DT), k-Nearest Neighbors (kNN), NaïveBayes (NB), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), to build a novel ensemble

Externí odkaz: https://doaj.org/article/4378f66e61f441ef8f0a29ae4f701870

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Akademický článek

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

Autor: Mohammad Ahmadlou, A'kif Al‐Fugara, Abdel Rahman Al‐Shabeeb, Aman Arora, Rida Al‐Adamat, Quoc Bao Pham, Nadhir Al‐Ansari, Nguyen Thi Thuy Linh, Hedieh Sajedi

Publikováno v: Journal of Flood Risk Management, Vol 14, Iss 1, Pp n/a-n/a (2021)

Abstract Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS)

Externí odkaz: https://doaj.org/article/e7496bb0f5294f31977d8376ad50df2a

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Akademický článek

Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

Autor: A’kif Al-Fugara, Ali Nouh Mabdeh, Mohammad Ahmadlou, Hamid Reza Pourghasemi, Rida Al-Adamat, Biswajeet Pradhan, Abdel Rahman Al-Shabeeb

Publikováno v: ISPRS International Journal of Geo-Information, Vol 10, Iss 6, p 382 (2021)

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system

Externí odkaz: https://doaj.org/article/91d088291d694511b06d4afe0c5e7e27

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Akademický článek

DSM derived stereo pair photogrammetry: Multitemporal morphometric analysis of a quarry in karst terrain

Autor: A’kif Al-Fugara, Rida Al-Adamat, Omar Al-Kouri, Saleh Taher

Publikováno v: Egyptian Journal of Remote Sensing and Space Sciences, Vol 19, Iss 1, Pp 61-72 (2016)

Bukit Merah karst has been deteriorating dramatically over 40 years due to intensification of human activities as a result of fast rate of lateral urbanization and extensive dimensional expansion of surface mining activities for instance, quarrying.

Externí odkaz: https://doaj.org/article/f100c0cf88044cddbf5be4de40dc8a95

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Combination of Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping

Autor: Saad AlAyyash, A’kif Al-Fugara, Rania Shatnawi, Abdel Rahman Al-Shabeeb, Rida Al-Adamat, Hani Al-Amoush

Publikováno v: Sustainability
Volume 15
Issue 3
Pages: 2499

The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27ea3f4f70b190d4ee0b44b0a88b920a

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Forest Fire Susceptibility Assessment and Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Evolutionary Algorithms

Autor: Ali Nouh Mabdeh, A’kif Al-Fugara, Khaled Mohamed Khedher, Muhammed Mabdeh, Abdel Rahman Al-Shabeeb, Rida Al-Adamat

Publikováno v: Sustainability; Volume 14; Issue 15; Pages: 9446

Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37799f514f9fafe95cd1b5f395ae3200

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Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping

Autor: Mohammad Ahmadlou, Sangeeta Soni, Rania S. Shatnawi, Rida Al-Adamat, A’kif Al-Fugara, Abdel Al-Rahman Al-Shabeeb, Saad AlAyyash

Publikováno v: Geocarto International. 37:2627-2646

This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b6d1a81cea4f20ad7234507afe0ff963
https://doi.org/10.1080/10106049.2020.1831622

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Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

Autor: A’kif Al-Fugara, Abdel Rahman Al-Shabeeb, Saad AlAyyash, Mohammad Ahmadlou, Hani Al-Amoush, Rida Al-Adamat

Publikováno v: Geocarto International. 37:284-303

In this study, groundwater springs potentiality maps were prepared using a novel integrated model, support vector regression (SVR) with genetic algorithm (GA), for the Jerash and Ajloun region, Jor...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::34c6ebf3131807176810d85bc72517f0
https://doi.org/10.1080/10106049.2020.1716396

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