全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:... Maryam Gharekhani , Rahman Khatibi , Asghar Asghari Moghaddam
来源:[J].Environmental Science and Pollution Research(IF 2.618), 2017, Vol.24 (9), pp.8562-8577Springer
摘要:Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any....
作者:Rahman Khatibi , Yousef Hassanzadeh ...
来源:[J].Arabian Journal for Science and Engineering, 2017, Vol.42 (9), pp.4169-4179Springer
摘要:The information content of the classic equations describing the problem of forced hydraulic jumps in open channels is the subject of this paper. The forcing refers to designed structural composites to transform incoming supercritical flows into outgoing subcritical flows thr...
作者:... Maryam Gharekhani , Rahman Khatibi , Elham Akbari
来源:[J].Journal of Environmental Management(IF 3.057), 2018, Vol.217, pp.654-667Elsevier
摘要:Abstract(#br)Proof-of-concept is presented in this paper to a methodology formulated for indexing risks to groundwater aquifers exposed to impacts of diffuse contaminations from anthropogenic and geogenic origins. The methodology is for mapping/indexing, which refers to relative ...
作者:... Zahra Sedghi , Rahman Khatibi , Sina Sadeghfam
来源:[J].Journal of Environmental Management(IF 3.057), 2018, Vol.227, pp.415-428Elsevier
摘要:Abstract(#br)An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic contamination. BDF pr...
作者:Rahman Khatibi , Sina Sadeghfam
来源:[J].Science of the Total Environment(IF 3.258), 2017, Vol.574, pp.691-706Elsevier
摘要:Abstract(#br)This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), N...
作者:... Hossein Norouzi , Rahman Khatibi , Maryam Gharekhani
来源:[J].Journal of Hydrology(IF 2.964), 2019, Vol.574, pp.744-759Elsevier
摘要:Abstract(#br)Production of defensible modelling tools for aquifer vulnerability mapping under the conditions of sparse data remains topical using the DRASTIC framework. DRASTIC is the acronym for seven data layers using a prescribed scoring system in terms of rates to accoun...
作者:Rahman Khatibi , Sina Sadeghfam
来源:[J].Science of the Total Environment(IF 3.258), 2017, Vol.574, pp.691-706Elsevier
摘要:Abstract(#br)This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), N...
作者:Rahman Khatibi , Ali Danandeh Mehr
来源:[J].Journal of Hydrology(IF 2.964), 2018, Vol.562, pp.455-467Elsevier
摘要:Abstract(#br)Chaos theory is integrated with Multi-Gene Genetic Programming (MGGP) engine as a new hybrid model for river flow forecasting. This is to be referred to as Chaos-MGGP and its performance is tested using daily historic flow time series at four gauging stations in two ...
作者:... Ali Ehsanitabar , Rahman Khatibi , Rasoul Daneshfaraz
来源:[J].Ecological Indicators(IF 2.89), 2018, Vol.94, pp.170-184Elsevier
摘要:Abstract(#br)A novel methodology is introduced for the spatial indexing of groundwater drought ‘risks’ (GDRs). It combines reliability analysis and standardised water-level index (SWI), which is readily applicable to areas with sparse data on groundwater depth (GWD) measurem...

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