Jayashree ChadalawadaNational University of Singapore | NUS · Department of Civil & Environmental Engineering
Jayashree Chadalawada
Doctor of Philosophy
About
16
Publications
4,617
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407
Citations
Introduction
Additional affiliations
December 2017 - May 2019
Education
August 2005 - April 2009
Government College of Technology
Field of study
- Civil Engineering
Publications
Publications (16)
Urban areas around the world are rapidly changing in an unregulated manner and remote sensing is the most effective option for their monitoring and planning. Good modeling of urban areas means reliable translation of the scene semantics into an algorithmic language. The compression based image retrieval techniques are data driven. The intention of...
Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to develo...
Despite showing a great success of applications in many commercial fields, machine learning and data science models in general, show a limited use in scientific fields including hydrology. The approach is often criticized for lack of interpretability and physical consistency. This has led to the emergence of new paradigms, such as Theory Guided Dat...
As new grid edge technologies emerge—such as rooftop solar panels, battery storage, and controllable water heaters—quantifying the uncertainties of building load forecasts is becoming more critical. The recent adoption of smart meter infrastructures provided new granular data streams, largely unavailable just ten years ago, that can be utilized to...
Despite showing great success of applications in many commercial fields, machine learning and data science models generally show limited success in many scientific fields, including hydrology (Karpatne et al., 2017). The approach is often criticized for its lack of interpretability and physical consistency. This has led to the emergence of new mode...
Relative dominance of the runoff controls, such as topography, geology, soil types, land use, and climate, may differ from catchment to catchment due to spatial and temporal heterogeneity of landscape properties and climate variables. Understanding dominant runoff controls is an essential task in developing unified hydrological theories at the catc...
Genetic programming (GP) is a widely used machine learning (ML) algorithm that has been applied in water resources science and engineering since its conception in the early 1990s. However, similar to other ML applications, the GP algorithm is often used as a data fitting tool rather than as a model building instrument. We find this a gross underuti...
The rainfall–runoff process is highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Various models have been developed to simulate this process, including lumped conceptual models, distributed physically based models, and empirical black-box models. Either conceptual or distributed physically based model...
The rainfall–runoff process is highly nonlinear, time varying, spatially distributed, and not easily described by simple models. Various models have been developed to simulate this process, including lumped conceptual models, distributed physically based models, and empirical black‐box models. Either conceptual or distributed physically based model...
One of the more perplexing challenges for the hydrologic research community is the need for development of coupled systems involving integration of hydrologic, atmospheric and socioeconomic relationships. Given the demand for integrated modelling and availability of enormous data with varying degrees of (un)certainty, there exists growing popularit...
Advancements in data acquisition, storage and retrieval are progressing at an extraordinary rate, whereas the same in the field of knowledge extraction from data is yet to be accomplished. The challenges associated with hydrological datasets, including complexity, non-linearity and multicollinearity, motivate the use of machine learning to build hy...
Collaboration between data driven and conceptual modelling paradigms
This paper presents a study of content based image retrieval using compression based methods with original and despeckled TerraSAR-X images. This study aims at analysing the behaviour of our method regarding speckle noise. Our method is based on Lempel-Ziv-Welch compression algorithm for feature extraction and fast compression distance as similarit...
Urban areas around the world are rapidly changing in an unregulated manner and remote sensing is the most effective option for their monitoring and planning. The wide variability in the organization of cities all over the world makes it difficult to make a global and accurate model for urban areas. Good modeling of urban areas means reliable transl...
Today, fly ash is being used as a structural fill material in highway and railway embankments, for ash pond bunds, levees, filling low-laying areas etc. Seepage induced failures in the form of piping can weaken and affect the performance of an embankment constructed with fly ash as a structural fill material. This paper presents a study to examine...