Search Results - "Rahmani, Farshid"

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data by Rahmani, Farshid, Lawson, Kathryn, Ouyang, Wenyu, Appling, Alison, Oliver, Samantha, Shen, Chaopeng

“…Stream water temperature (Ts) is a variable of critical importance for aquatic ecosystem health. Ts is strongly affected by groundwater-surface water…”
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A Conceptual Model for Selecting Early Contractor Involvement (ECI) for a Project by Rahmani, Farshid, Khalfan, Malik, Maqsood, Tayyab

“…Amongst different aspects of a capital construction project, procurement is found to be the most important area and represents over 80% of the contract value…”
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AgiBuild: A Scaled Agile Framework for Building Adaptation Projects by Ng, Pearl Li, Maqsood, Tayyab, Khalfan, Malik, Rahmani, Farshid

“…Agile ways of working have garnered recognition for their capacity to drive innovation, placing a strong emphasis on adaptability to change and a user-centric…”
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Challenges and opportunities in adopting early contractor involvement (ECI): client's perception by Rahmani, Farshid

“…The emerging project delivery methods increasingly encourage collaboration between the client, designer and contractor to develop longer-term relationship…”
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Lessons learnt from the use of relationship-based procurement methods in Australia: Clients' perspectives by Rahmani, Farshid, Khalfan, Malik M A, Maqsood, Tayyab

“…This paper aims to review the use of various construction procurement systems and present the development of Relationship-Based Procurement (RBP) Methods…”
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Abductive Grounded Theory: a worked example of a study in construction management by Rahmani, Farshid, Leifels, Katrin

“…Grounded Theory, now more than 50 years old, is a qualitative research approach widely employed in the social and human science studies to develop theories…”
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Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins by Rahmani, Farshid, Shen, Chaopeng, Oliver, Samantha, Lawson, Kathryn, Appling, Alison

“…Basin‐centric long short‐term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal…”
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A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data by Liu, Jiangtao, Rahmani, Farshid, Lawson, Kathryn, Shen, Chaopeng

“…Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as…”
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Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling by Rahmani, Farshid, Appling, Alison, Feng, Dapeng, Lawson, Kathryn, Shen, Chaopeng

“…Abstract Although deep learning models for stream temperature ( T s ) have recently shown exceptional accuracy, they have limited interpretability and cannot…”
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A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds by Saha, Gourab Kumer, Rahmani, Farshid, Shen, Chaopeng, Li, Li, Cibin, Raj

“…High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions…”
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A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data by Liu, Jiangtao, Rahmani, Farshid, Lawson, Kathryn, Shen, Chaopeng

“…Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as…”
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Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration by Khoshkalam, Yegane, Rousseau, Alain N., Rahmani, Farshid, Shen, Chaopeng, Abbasnezhadi, Kian

“…•Transfer learning can realize the benefits of integrating recent discharge during streamflow prediction.•Addition of a physically based model output as an…”
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Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations by Song, Yalan, Chaemchuen, Piyaphat, Rahmani, Farshid, Zhi, Wei, Li, Li, Liu, Xiaofeng, Boyer, Elizabeth, Bindas, Tadd, Lawson, Kathryn, Shen, Chaopeng

“…•LSTM predicts daily SSC well using basin-scale atmospheric forcings, attributes, and observed or modeled streamflow.•Whole-CONUS LSTM can provide reliable…”
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Optimal Operation of Water Distribution Systems Using a Graph Theory–Based Configuration of District Metered Areas by Rahmani, Farshid, Muhammed, Karwan, Behzadian, Kourosh, Farmani, Raziyeh

“…AbstractOptimal operation of a large water distribution system (WDS) has always been a tedious task, especially when combined with determination of district…”
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Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning by Bindas, Tadd, Tsai, Wen‐Ping, Liu, Jiangtao, Rahmani, Farshid, Feng, Dapeng, Bian, Yuchen, Lawson, Kathryn, Shen, Chaopeng

“…Recently, rainfall‐runoff simulations in small headwater basins have been improved by methodological advances such as deep neural networks (NNs) and hybrid…”
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Rehabilitation of a Water Distribution System Using Sequential Multiobjective Optimization Models by Rahmani, Farshid, Behzadian, Kourosh, Ardeshir, Abdollah

“…AbstractIdentification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a…”
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An overview of construction procurement methods in Australia by Rahmani, Farshid, Maqsood, Tayyab, Khalfan, Malik

“…Purpose The purpose of this paper is to review the use of various construction procurement systems in the past and present, specifically within the Australian…”
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Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats by Liu, Jiangtao, Hughes, David, Rahmani, Farshid, Lawson, Kathryn, Shen, Chaopeng

“…Climate change threatens our ability to grow food for an ever-increasing population. There is a need for high-quality soil moisture predictions in…”
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Differentiable modelling to unify machine learning and physical models for geosciences by Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Song, Yalan, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Mohanty, Binayak, Roy, Tirthankar, Xu, Chonggang, Lawson, Kathryn

“…Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently…”
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Team composition in relational contracting (RC) in large infrastructure projects: a Belbin’s team roles model approach by Rahmani, Farshid, Scott-Young, Christina, Tadayon, Allen, van der Walt, Jacobus Daniel

“…PurposeThe aim of this study is to broaden the understanding of the set of knowledge, skills, attributes and experience (KSAE) that teams should demonstrate…”
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