Sanaz Imen | University of Central Florida (original) (raw)

Papers by Sanaz Imen

Research paper thumbnail of What does landslide triggering rainfall mean?

E3S Web of Conferences

Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides wh... more Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and dur...

Research paper thumbnail of Application of machine learning at wastewater treatment facilities: a review of the science, challenges and barriers by level of implementation

Environmental Technology Reviews

Research paper thumbnail of Applications of GIS and remote sensing in public participation and stakeholder engagement for watershed management

Socio-Environmental Systems Modelling

The use of Geographic Information Systems (GIS) and remote sensing technologies for the developme... more The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented ...

Research paper thumbnail of Watershed Models

Total Maximum Daily Load Development and Implementation, Feb 24, 2022

Research paper thumbnail of Model Data, Geographical Information Systems, and Remote Sensing

Total Maximum Daily Load Development and Implementation, 2022

Research paper thumbnail of Developing a Model-Based Drinking Water Decision Support System Featuring Remote Sensing and Fast Learning Techniques

IEEE Systems Journal, 2016

Timely adjustment of operating strategies in drinking water treatment in response to water qualit... more Timely adjustment of operating strategies in drinking water treatment in response to water quality variations of both natural and anthropogenic causes is a grand technical challenge. One essential approach is to develop and apply integrated sensing, monitoring, and modeling technologies to provide early warning messages to plant operators. This paper presents a thorough literature review of the technical methods, followed by the development of a model-based decision support system (DSS). The DSS aims to aid water treatment operation via source water impact analysis. This model-based DSS featuring remote sensing and fast learning techniques can be easily applied by end-users and provide a visual depiction of spatiotemporal variation in water quality parameters of interest in source water. The system is able to forecast the trend of water quality one day into the future at a specific location and to nowcast water quality at water intake, thus helping the assessment of water quality in finished water against treatment objectives. The model-based DSS was assessed in a case study at a water treatment plant in Las Vegas, United States.

Research paper thumbnail of Total Maximum Daily Load Development and Implementation

Research paper thumbnail of Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

Remote Sensing, 2016

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This r... more The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

Research paper thumbnail of Developing a cyber-physical system for smart and sustainable drinking water infrastructure management

2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), 2016

Frequent adjustment of operating strategies in water treatment plant and water distribution netwo... more Frequent adjustment of operating strategies in water treatment plant and water distribution network as a simultaneous response to growing water scarcity has been a grand challenge. This challenge is emanated from transitioning the sporadic water quality samplings to self-awareness, self-adaptive, and fast response system. To bridge this gap, a cyber-physical system (CPS) is developed in this study to respond to the needs of smart and sustainable drinking water infrastructure management. This new CPS is able to gather the massive volumes of information from ground and aquatic reference data via advanced remote sensing and sensor network technologies to timely detect water pollution, exchange information through cyber interfaces, provide early-warning awareness with the aid of different models, and support actionable intelligence. Integrated 5-level CPS architecture is proposed in this study as an instruction of developing CPS for smart and sustainable drinking water infrastructure management.

Research paper thumbnail of Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring

IEEE Systems Journal, 2016

Given the advancements of remote sensing technology, large volumes of remotely sensed images with... more Given the advancements of remote sensing technology, large volumes of remotely sensed images with different spatial, temporal, and spectral resolutions are available. To better monitor and understand the changing Earth's environment, fusion of remotely sensed images with different spatial, temporal, and spectral resolutions is critical for distinctive feature retrieval, interpretation, mapping, and decision analysis. A suite of methods have been developed to fuse multisensor satellite images for different purposes in the past few decades. This paper provides a thorough review of contemporary and classic image fusion methods and presents a summary of their phenomenological applications, with challenges and perspectives, for environmental systems analysis. Cross-mission satellite image fusion, networking, and missing value pixel reconstruction for environmental monitoring are described, and their complex integration is illustrated with a case study of Lake Nicaragua that elucidates the state-of-the-art remote sensing technologies for advancing water quality management.

Research paper thumbnail of Multi-sensor Acquisition, Data Fusion, Criteria Mining and Alarm Triggering for Decision Support in Urban Water Infrastructure Systems

2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015

Frequent adjustment of the drinking water treatment process as a simultaneous response to climate... more Frequent adjustment of the drinking water treatment process as a simultaneous response to climate variations, and the impact those variations have on water quality, has been a grand challenge in water resource management in recent years. An early warning system with the aid of satellite remote sensing and local sensor networks, which provides timely and quantitative knowledge to monitor the quality of water, may be a soluition to this challenge. The development of such an early warning system is addressed to discover and evaluate the severity in a discrete event mode in this paper. The early warning system in the current study is able to empower the urban water ifrastructure systems with the integration of advanced data science, environmental monitoring, computational intelligence, and satellite remote sensing data. By developing a graphical user interface, end-users who do not have knowledge or skill in the field of integrated sensing, monitoring, networking, modeling can take advantage of the user-friendly early warning system. Practical implementation of the proposed early warning system was assessed at the largest resrvoir, Lake Mead, in Las Vegas in the United States. It uniquely demonstrates how such a system can benefit the drinking water treatment plant throughout decision support actions via multi-sensor acquisition, data fusion, criteria mining and alarm trigerring.

Research paper thumbnail of Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

Journal of Hydrology, 2017

Global sea surface temperature (SST) anomalies are observed to have a significant effect on terre... more Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intraannual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

Research paper thumbnail of Assessment of Uncertainty in Flood Forecasting Using Probabilistic and Fuzzy Approaches

(In 2 Volumes, with CD-ROM), 2004

Assigning uncertainty in flood forecasts increases the credibility of the forecasts and provides ... more Assigning uncertainty in flood forecasts increases the credibility of the forecasts and provides a rational basis for decision-making. Among various sources of uncertainty in flood forecasting, a large portion of it comes from the uncertainty in observed or forecasted ...

Research paper thumbnail of Development of an Algorithm for Contingency Planning in Dry Period

World Environmental and Water Resources Congress 2008, 2008

In order to minimize the impacts of drought and its consequences, Integrated Water Resources Mana... more In order to minimize the impacts of drought and its consequences, Integrated Water Resources Management (IWRM) is needed in a region with multifaceted water dependencies. A prerequisite to IWRM is the sound management of water resources system components. Reservoirs ...

Research paper thumbnail of Teleconnection Signals Effect on Terrestrial Precipitation: Big Data Analytics vs. Wavelet Analysis

Purpose: • Determine the associations between hydro-climatic variables and the atmospheric / ocea... more Purpose: • Determine the associations between hydro-climatic variables and the atmospheric / oceanic variables separated by large distances, which are known as the phenomenon of hydro-climatic teleconnection. • Discover physically meaningful patterns from big climate databases. Methodology: develop efficient data-driven approaches with the aid of machine learning, signal processing, and domain knowledge for constrained search. • Big Data Analytics: extract hydro-climatic variables from large temporal and spatial feature space and formulate the global search for teleconnection signals effect on terrestrial precipitation as feature selection in machine learning aspect. • Wavelet Analysis: retrieve the scale-averaged wavelet power to signify the teleconnection signals via a pixel-wise linear lagged correlation analysis. Introduction

Research paper thumbnail of Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

Adjustment of the drinking water treatment process as a simultaneous response to climate variatio... more Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region-Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to Hartshorn, Subrina Tahsin, and Justin Joyce. In particular, I would like to thank Kaixu Bai, Carolina Doña Monzó, Jamie Jones, Ben Vannah, and Lee Mullon. They've become more than colleagues, they are my best friends and I consider them a part of my extended family. Special thanks to my fiancé, who was always standing by me, with his love and support, in my hard time during this work. Finally, I would like to thank my family in Iran for their love and support. I would like to dedicate this dissertation to my grandmother, my mom, my dad, and my sister for all the love, support, and encouragement that they have given me over the years. I undoubtedly could not accomplish this goal without you. vi

Research paper thumbnail of Precipitation Forecasting With Wavelet-Based Empirical Orthogonal Function And Artificial Neural Network (WEOF-ANN) Model

Since 2000, western drought caused sharp drop by about 100 feet in the largest reservoir of North... more Since 2000, western drought caused sharp drop by about 100 feet in the largest reservoir of North America, Lake Mead. About 97% of inflow into Lake Mead is supplied by Colorado River Basin which is extremely sensitive to changes in precipitation and temperature. Oceans play an important role on earth's climate via oceanic-atmospheric interactions known as climate teleconnections, which deeply affect the terrestrial precipitation patterns. This issue signifies the necessity of developing a modern hydroinformatics tool-precipitation forecasting model-to account for teleconnection signals from climate change and mitigate drought hazards impact on lake water, quantitatively and qualitatively, which cannot be achieved by using traditional Global Circulation Model. Therefore, understanding the relationship between precipitation and teleconnection patterns could be the first step for precipitation forecasting. However, highly non-linear and non-stationary nature of teleconnection patterns result in large uncertainties in estimates, since simple linear analyses failed to capture underlying trends at sub-continental scales. For this purpose, high-resolution remote sensing imagery, spectral analysis techniques, and wavelet analysis were integrated to explore the nonstationary and nonlinear behavior of teleconnection signals between the Pacific and Atlantic sea surface temperature (SST) on a short-term basis (10 years) from which the precipitation pattern shift in the Upper Colorado River Basin can be elucidated. These processes lead to the creation of correlation maps which specify index regions within the Atlantic and Pacific Oceans where SST anomaly can be statistically significant in correlation with terrestrial precipitation. These indexed regions delivering some kind of memory effects of SST were extracted to be inputs into an Artificial Neural Network (ANN). Advances in Wavelet-based Empirical Orthogonal Function and Artificial Neural Network (WEOF-ANN) model for rainfall prediction assists the local water management agencies to mitigate the drought impacts and obtain sustainable development strategies a month ahead of the time in urban drinking water infrastructure assessment plan around Lake Mead area.

Research paper thumbnail of Improving the control of water treatment plant with remote sensing-based water quality forecasting model

When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinkin... more When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinking water treatment process, it oftentimes causes the formation of disinfection byproducts such as Trihalomethanes which have harmful effects on human health. As a result of the potential health risk of Trihalomethanes for drinking water, proper monitoring and forecasting of high TOC episodes in the source water body can be helpful for the operators who are in charge of the decisions when they have to start the removal procedures for TOC in surface water treatment plants. This issue is of great importance in Lake Mead in the United States which provides drinking water for 25 million people, while it is considered as an important recreational area and wildlife habitat as well. In this study, artificial neural network, extreme learning machine, and genetic programming are examined using the long-term observations of TOC concentration throughout the lake. Among these models, the model with th...

Research paper thumbnail of Impacts of global non-leading teleconnection signals on terrestrial precipitation across the United States

Remote Sensing and Modeling of Ecosystems for Sustainability XII, 2015

Identification of teleconnection patterns at a local scale is challenging, largely due to the coe... more Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. This study develops a method to overcome the problem of non-stationarity and nonlinearity and investigates how the non-leading teleconnection signals as well as the known teleconnection patterns can affect precipitation over three pristine sites in the United States. It is presented here that the oceanic indices which affect precipitation of specific site do not have commonality in different seasons. Results also found cases in which precipitation is significantly affected by the oceanic regions of two oceans within the same season. We attribute these cases to the combined physical oceanic-atmospheric processes caused by the coupled effects of oceanic regions. Interestingly, in some seasons, different regions in the South Pacific and Atlantic Oceans show more salient effects on precipitation compared to the known teleconnection patterns. Results highlight the importance of considering the seasonality scale and non-leading teleconnection signals in climate prediction.

Research paper thumbnail of Spatiotemporal monitoring of TOC concentrations in lake mead with a near real-time multi-sensor network

Forest fires, soil erosion, and land use changes in watersheds nearby Lake Mead and inflows from ... more Forest fires, soil erosion, and land use changes in watersheds nearby Lake Mead and inflows from Las Vegas Wash into the lake are considered as sources of the lake's water quality impairment. These conditions result in higher concentration of Total Organic Carbon (TOC). TOC in contact with Chlorine which is often used for disinfection purposes of drinking water supply causes the formation of trihalomethanes (THMs). THM is one of the toxic carcinogens controlled by the EPA's disinfection by-product rule. As a result of the threat posed to the drinking water used by the 25 million people downstream, recreational area, and wildlife habitat of Lake Mead, it is necessary to develop a method for near real-time monitoring of TOC in this area. Monitoring through a limited number of ground-based monitoring stations on a weekly/monthly basis is insufficient to capture both spatial and temporal variations of water quality changes. In this study, the multi-sensor remote sensing technology linking those ground-based TOC analyzers and two satellites with the aid of data fusion and mining techniques provides us with near real time information about the spatiotemporal distribution of TOC for the entire lake on a daily basis. A data fusion method was applied to bridge the gap of poor 250/500m spatial resolution for the land bands of Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the 30 m enhanced spatial resolution of Landsat's imageries which suffers from long overpass of 16 days. Consequently, near-real time Integrated Multi-sensor Fusion and Mining (IDFM) techniques produce synthetic fused images of MODIS and Landsat satellites with both high spatial and temporal resolution in order to create near-real time TOC distribution maps updated by ground-based TOC analyzers and lead to sustainable water quality management with the aid of IDFM in Lake Mead watershed.

Research paper thumbnail of What does landslide triggering rainfall mean?

E3S Web of Conferences

Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides wh... more Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and dur...

Research paper thumbnail of Application of machine learning at wastewater treatment facilities: a review of the science, challenges and barriers by level of implementation

Environmental Technology Reviews

Research paper thumbnail of Applications of GIS and remote sensing in public participation and stakeholder engagement for watershed management

Socio-Environmental Systems Modelling

The use of Geographic Information Systems (GIS) and remote sensing technologies for the developme... more The use of Geographic Information Systems (GIS) and remote sensing technologies for the development of water quality management programs and for post-implementation assessments has increased dramatically in the past decade. This increase in adoption has been made more accessible through the interfaces of many popular software tools used in the regulation and assessment of water quality. Customized applications of these tools will increase, as ease of access and affordability of directly monitored and remotely sensed datasets improve over time. Concurrently, there is a need for inclusive participatory engagement with stakeholders to achieve solutions to current watershed management challenges. This paper explores the potential of these GIS and remote sensing datasets, tools, models, and immersive engagement technologies from other domains, for improving public participation and stakeholder engagement throughout the watershed planning process. To do so, an initial review is presented ...

Research paper thumbnail of Watershed Models

Total Maximum Daily Load Development and Implementation, Feb 24, 2022

Research paper thumbnail of Model Data, Geographical Information Systems, and Remote Sensing

Total Maximum Daily Load Development and Implementation, 2022

Research paper thumbnail of Developing a Model-Based Drinking Water Decision Support System Featuring Remote Sensing and Fast Learning Techniques

IEEE Systems Journal, 2016

Timely adjustment of operating strategies in drinking water treatment in response to water qualit... more Timely adjustment of operating strategies in drinking water treatment in response to water quality variations of both natural and anthropogenic causes is a grand technical challenge. One essential approach is to develop and apply integrated sensing, monitoring, and modeling technologies to provide early warning messages to plant operators. This paper presents a thorough literature review of the technical methods, followed by the development of a model-based decision support system (DSS). The DSS aims to aid water treatment operation via source water impact analysis. This model-based DSS featuring remote sensing and fast learning techniques can be easily applied by end-users and provide a visual depiction of spatiotemporal variation in water quality parameters of interest in source water. The system is able to forecast the trend of water quality one day into the future at a specific location and to nowcast water quality at water intake, thus helping the assessment of water quality in finished water against treatment objectives. The model-based DSS was assessed in a case study at a water treatment plant in Las Vegas, United States.

Research paper thumbnail of Total Maximum Daily Load Development and Implementation

Research paper thumbnail of Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

Remote Sensing, 2016

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This r... more The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

Research paper thumbnail of Developing a cyber-physical system for smart and sustainable drinking water infrastructure management

2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC), 2016

Frequent adjustment of operating strategies in water treatment plant and water distribution netwo... more Frequent adjustment of operating strategies in water treatment plant and water distribution network as a simultaneous response to growing water scarcity has been a grand challenge. This challenge is emanated from transitioning the sporadic water quality samplings to self-awareness, self-adaptive, and fast response system. To bridge this gap, a cyber-physical system (CPS) is developed in this study to respond to the needs of smart and sustainable drinking water infrastructure management. This new CPS is able to gather the massive volumes of information from ground and aquatic reference data via advanced remote sensing and sensor network technologies to timely detect water pollution, exchange information through cyber interfaces, provide early-warning awareness with the aid of different models, and support actionable intelligence. Integrated 5-level CPS architecture is proposed in this study as an instruction of developing CPS for smart and sustainable drinking water infrastructure management.

Research paper thumbnail of Multisensor Satellite Image Fusion and Networking for All-Weather Environmental Monitoring

IEEE Systems Journal, 2016

Given the advancements of remote sensing technology, large volumes of remotely sensed images with... more Given the advancements of remote sensing technology, large volumes of remotely sensed images with different spatial, temporal, and spectral resolutions are available. To better monitor and understand the changing Earth's environment, fusion of remotely sensed images with different spatial, temporal, and spectral resolutions is critical for distinctive feature retrieval, interpretation, mapping, and decision analysis. A suite of methods have been developed to fuse multisensor satellite images for different purposes in the past few decades. This paper provides a thorough review of contemporary and classic image fusion methods and presents a summary of their phenomenological applications, with challenges and perspectives, for environmental systems analysis. Cross-mission satellite image fusion, networking, and missing value pixel reconstruction for environmental monitoring are described, and their complex integration is illustrated with a case study of Lake Nicaragua that elucidates the state-of-the-art remote sensing technologies for advancing water quality management.

Research paper thumbnail of Multi-sensor Acquisition, Data Fusion, Criteria Mining and Alarm Triggering for Decision Support in Urban Water Infrastructure Systems

2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015

Frequent adjustment of the drinking water treatment process as a simultaneous response to climate... more Frequent adjustment of the drinking water treatment process as a simultaneous response to climate variations, and the impact those variations have on water quality, has been a grand challenge in water resource management in recent years. An early warning system with the aid of satellite remote sensing and local sensor networks, which provides timely and quantitative knowledge to monitor the quality of water, may be a soluition to this challenge. The development of such an early warning system is addressed to discover and evaluate the severity in a discrete event mode in this paper. The early warning system in the current study is able to empower the urban water ifrastructure systems with the integration of advanced data science, environmental monitoring, computational intelligence, and satellite remote sensing data. By developing a graphical user interface, end-users who do not have knowledge or skill in the field of integrated sensing, monitoring, networking, modeling can take advantage of the user-friendly early warning system. Practical implementation of the proposed early warning system was assessed at the largest resrvoir, Lake Mead, in Las Vegas in the United States. It uniquely demonstrates how such a system can benefit the drinking water treatment plant throughout decision support actions via multi-sensor acquisition, data fusion, criteria mining and alarm trigerring.

Research paper thumbnail of Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models

Journal of Hydrology, 2017

Global sea surface temperature (SST) anomalies are observed to have a significant effect on terre... more Global sea surface temperature (SST) anomalies are observed to have a significant effect on terrestrial precipitation patterns throughout the United States. SST variations have been correlated with terrestrial precipitation via ocean-atmospheric interactions known as climate teleconnections. This study demonstrates how the scale effect could affect the forecasting accuracy with or without the inclusion of those newly discovered unknown teleconnection signals between Adirondack precipitation and SST anomaly in the Atlantic and Pacific oceans. Unique SST regions of both known and unknown telecommunication signals were extracted from the wavelet analysis and used as input variables in an artificial neural network (ANN) forecasting model. Monthly and seasonal scales were considered with respect to a host of long-term (30-year) nonlinear and nonstationary teleconnection signals detected locally at the study site of Adirondack. Similar intraannual time-lag effects of SST on precipitation variability are salient at both time scales. Sensitivity analysis of four scenarios reveals that more improvements of the forecasting accuracy of the ANN model can be observed by including both known and unknown teleconnection patterns at both time scales, although such improvements are not salient. Research findings also highlight the importance of choosing the forecasting model at the seasonal scale to predict more accurate peak values and global trends of terrestrial precipitation in response to teleconnection signals. The scale shift from monthly to seasonal may improve results by 17% and 17 mm/day in terms of R squared and root of mean square error values, respectively, if both known and unknown SST regions are considered for forecasting.

Research paper thumbnail of Assessment of Uncertainty in Flood Forecasting Using Probabilistic and Fuzzy Approaches

(In 2 Volumes, with CD-ROM), 2004

Assigning uncertainty in flood forecasts increases the credibility of the forecasts and provides ... more Assigning uncertainty in flood forecasts increases the credibility of the forecasts and provides a rational basis for decision-making. Among various sources of uncertainty in flood forecasting, a large portion of it comes from the uncertainty in observed or forecasted ...

Research paper thumbnail of Development of an Algorithm for Contingency Planning in Dry Period

World Environmental and Water Resources Congress 2008, 2008

In order to minimize the impacts of drought and its consequences, Integrated Water Resources Mana... more In order to minimize the impacts of drought and its consequences, Integrated Water Resources Management (IWRM) is needed in a region with multifaceted water dependencies. A prerequisite to IWRM is the sound management of water resources system components. Reservoirs ...

Research paper thumbnail of Teleconnection Signals Effect on Terrestrial Precipitation: Big Data Analytics vs. Wavelet Analysis

Purpose: • Determine the associations between hydro-climatic variables and the atmospheric / ocea... more Purpose: • Determine the associations between hydro-climatic variables and the atmospheric / oceanic variables separated by large distances, which are known as the phenomenon of hydro-climatic teleconnection. • Discover physically meaningful patterns from big climate databases. Methodology: develop efficient data-driven approaches with the aid of machine learning, signal processing, and domain knowledge for constrained search. • Big Data Analytics: extract hydro-climatic variables from large temporal and spatial feature space and formulate the global search for teleconnection signals effect on terrestrial precipitation as feature selection in machine learning aspect. • Wavelet Analysis: retrieve the scale-averaged wavelet power to signify the teleconnection signals via a pixel-wise linear lagged correlation analysis. Introduction

Research paper thumbnail of Drinking Water Infrastructure Assessment with Teleconnection Signals, Satellite Data Fusion and Mining

Adjustment of the drinking water treatment process as a simultaneous response to climate variatio... more Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region-Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to Hartshorn, Subrina Tahsin, and Justin Joyce. In particular, I would like to thank Kaixu Bai, Carolina Doña Monzó, Jamie Jones, Ben Vannah, and Lee Mullon. They've become more than colleagues, they are my best friends and I consider them a part of my extended family. Special thanks to my fiancé, who was always standing by me, with his love and support, in my hard time during this work. Finally, I would like to thank my family in Iran for their love and support. I would like to dedicate this dissertation to my grandmother, my mom, my dad, and my sister for all the love, support, and encouragement that they have given me over the years. I undoubtedly could not accomplish this goal without you. vi

Research paper thumbnail of Precipitation Forecasting With Wavelet-Based Empirical Orthogonal Function And Artificial Neural Network (WEOF-ANN) Model

Since 2000, western drought caused sharp drop by about 100 feet in the largest reservoir of North... more Since 2000, western drought caused sharp drop by about 100 feet in the largest reservoir of North America, Lake Mead. About 97% of inflow into Lake Mead is supplied by Colorado River Basin which is extremely sensitive to changes in precipitation and temperature. Oceans play an important role on earth's climate via oceanic-atmospheric interactions known as climate teleconnections, which deeply affect the terrestrial precipitation patterns. This issue signifies the necessity of developing a modern hydroinformatics tool-precipitation forecasting model-to account for teleconnection signals from climate change and mitigate drought hazards impact on lake water, quantitatively and qualitatively, which cannot be achieved by using traditional Global Circulation Model. Therefore, understanding the relationship between precipitation and teleconnection patterns could be the first step for precipitation forecasting. However, highly non-linear and non-stationary nature of teleconnection patterns result in large uncertainties in estimates, since simple linear analyses failed to capture underlying trends at sub-continental scales. For this purpose, high-resolution remote sensing imagery, spectral analysis techniques, and wavelet analysis were integrated to explore the nonstationary and nonlinear behavior of teleconnection signals between the Pacific and Atlantic sea surface temperature (SST) on a short-term basis (10 years) from which the precipitation pattern shift in the Upper Colorado River Basin can be elucidated. These processes lead to the creation of correlation maps which specify index regions within the Atlantic and Pacific Oceans where SST anomaly can be statistically significant in correlation with terrestrial precipitation. These indexed regions delivering some kind of memory effects of SST were extracted to be inputs into an Artificial Neural Network (ANN). Advances in Wavelet-based Empirical Orthogonal Function and Artificial Neural Network (WEOF-ANN) model for rainfall prediction assists the local water management agencies to mitigate the drought impacts and obtain sustainable development strategies a month ahead of the time in urban drinking water infrastructure assessment plan around Lake Mead area.

Research paper thumbnail of Improving the control of water treatment plant with remote sensing-based water quality forecasting model

When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinkin... more When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinking water treatment process, it oftentimes causes the formation of disinfection byproducts such as Trihalomethanes which have harmful effects on human health. As a result of the potential health risk of Trihalomethanes for drinking water, proper monitoring and forecasting of high TOC episodes in the source water body can be helpful for the operators who are in charge of the decisions when they have to start the removal procedures for TOC in surface water treatment plants. This issue is of great importance in Lake Mead in the United States which provides drinking water for 25 million people, while it is considered as an important recreational area and wildlife habitat as well. In this study, artificial neural network, extreme learning machine, and genetic programming are examined using the long-term observations of TOC concentration throughout the lake. Among these models, the model with th...

Research paper thumbnail of Impacts of global non-leading teleconnection signals on terrestrial precipitation across the United States

Remote Sensing and Modeling of Ecosystems for Sustainability XII, 2015

Identification of teleconnection patterns at a local scale is challenging, largely due to the coe... more Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. This study develops a method to overcome the problem of non-stationarity and nonlinearity and investigates how the non-leading teleconnection signals as well as the known teleconnection patterns can affect precipitation over three pristine sites in the United States. It is presented here that the oceanic indices which affect precipitation of specific site do not have commonality in different seasons. Results also found cases in which precipitation is significantly affected by the oceanic regions of two oceans within the same season. We attribute these cases to the combined physical oceanic-atmospheric processes caused by the coupled effects of oceanic regions. Interestingly, in some seasons, different regions in the South Pacific and Atlantic Oceans show more salient effects on precipitation compared to the known teleconnection patterns. Results highlight the importance of considering the seasonality scale and non-leading teleconnection signals in climate prediction.

Research paper thumbnail of Spatiotemporal monitoring of TOC concentrations in lake mead with a near real-time multi-sensor network

Forest fires, soil erosion, and land use changes in watersheds nearby Lake Mead and inflows from ... more Forest fires, soil erosion, and land use changes in watersheds nearby Lake Mead and inflows from Las Vegas Wash into the lake are considered as sources of the lake's water quality impairment. These conditions result in higher concentration of Total Organic Carbon (TOC). TOC in contact with Chlorine which is often used for disinfection purposes of drinking water supply causes the formation of trihalomethanes (THMs). THM is one of the toxic carcinogens controlled by the EPA's disinfection by-product rule. As a result of the threat posed to the drinking water used by the 25 million people downstream, recreational area, and wildlife habitat of Lake Mead, it is necessary to develop a method for near real-time monitoring of TOC in this area. Monitoring through a limited number of ground-based monitoring stations on a weekly/monthly basis is insufficient to capture both spatial and temporal variations of water quality changes. In this study, the multi-sensor remote sensing technology linking those ground-based TOC analyzers and two satellites with the aid of data fusion and mining techniques provides us with near real time information about the spatiotemporal distribution of TOC for the entire lake on a daily basis. A data fusion method was applied to bridge the gap of poor 250/500m spatial resolution for the land bands of Moderate Resolution Imaging Spectroradiometer (MODIS) imageries with the 30 m enhanced spatial resolution of Landsat's imageries which suffers from long overpass of 16 days. Consequently, near-real time Integrated Multi-sensor Fusion and Mining (IDFM) techniques produce synthetic fused images of MODIS and Landsat satellites with both high spatial and temporal resolution in order to create near-real time TOC distribution maps updated by ground-based TOC analyzers and lead to sustainable water quality management with the aid of IDFM in Lake Mead watershed.