Inicio  /  Water  /  Vol: 12 Par: 6 (2020)  /  Artículo
ARTÍCULO
TITULO

Uncertainty Quantification in Water Resource Systems Modeling: Case Studies from India

Shaik Rehana    
Chandra Rupa Rajulapati    
Subimal Ghosh    
Subhankar Karmakar and Pradeep Mujumdar    

Resumen

Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.

 Artículos similares

       
 
Z. Jason Hou, Nicholas D. Ward, Allison N. Myers-Pigg, Xinming Lin, Scott R. Waichler, Cora Wiese Moore, Matthew J. Norwood, Peter Regier and Steven B. Yabusaki    
The influence of coastal ecosystems on global greenhouse gas (GHG) budgets and their response to increasing inundation and salinization remains poorly constrained. In this study, we have integrated an uncertainty quantification (UQ) and ensemble machine ... ver más
Revista: Water

 
Haohao Wang, Limin Gao and Baohai Wu    
Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the amounts of data collected as to compressor blad... ver más
Revista: Aerospace

 
Vishnupriya Jonnalagadda, Ji Yun Lee, Jie Zhao and Seyed Hooman Ghasemi    
The nation?s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic ... ver más
Revista: Infrastructures

 
Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu    
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DN... ver más
Revista: Algorithms

 
Andreas Nugaard Holm, Dustin Wright and Isabelle Augenstein    
Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) dropout, which is computationally expensi... ver más
Revista: Information