Weighted Linear Regression Research Articles

Shoreline Change Assessment in the Orashi River, Rivers State, Nigeria, using the Digital Shoreline Analysis System (DSAS)

Shoreline change profoundly impacts coastal geology and coastal communities. This study, spanning three decades from 1992 to 2022, employed the Digital Shoreline Analysis System (DSAS) to assess the Orashi River shoreline dynamics in Nigeria. Using Landsat imagery data from the United States Geological Survey (USGS) and ArcGIS 10.6, DSAS 5.0 software was utilized for analysis. Various statistical methods, including Linear Regression Rate (LRR), Weighted Linear Regression (WLR), End Point Rate (EPR), Net Shoreline Movement (NSM), and Shoreline Change Envelope (SCE), quantified shoreline changes. The findings consistently reveal significant shoreline regression. LRR, -1.78 to -1.07, highlights continuous landward movement, signifying erosion’s persistent impact. WLR rates reaffirm this trend, closely mirroring the range of -1.78 to -1.07. EPR, -2.79 to -1.29, further underline the erosional trend, emphasizing substantial inland shifts. NSM, ranging from -48.66 to -20.30, signifies substantial shoreline retreat, showcasing erosion’s profound effect. The NSM, varying from 0.69 to -11.50, illustrates dynamic shoreline movement with varying degrees of erosion. The outcomes identify prevalent erosion, particularly in Very High Erosion (VHE) areas, while High Accretion (HA) and Very High Accretion (VHA) zones experience shoreline gain. These classifications inform coastal management strategies, urging protective measures for VHE areas and development opportunities in HA and VHA zones. The variability in shoreline change underscores coastal dynamics’ complexity. This study contributes to understanding the Orashi River’s shoreline processes, guiding sustainable coastal management and environmental conservation efforts. Continued monitoring and research are crucial for adapting to the evolving coastal landscape’s impact on the environment and local communities.

Association between monocyte to high-density lipoprotein-cholesterol ratio and osteoporosis: An analysis of the National Health and Nutrition Examination Survey 2013-2014.

The monocyte to high-density lipoprotein-cholesterol (HDL-C) ratio (monocyte-to-HDL-C ratio) was proposed as a marker of atherosclerosis. Osteoporosis and atherosclerosis share common risk factors and pathophysiological mechanisms. This study aimed to assess the relationship between monocyte-to-HDL-C ratio and osteoporosis. Participants aged ≥50 years with complete bone mineral density (BMD), monocyte, and HDL-C examination data from the National Health and Nutrition Examination Survey (NHANES) 2013-2014 were included. Descriptive analysis was performed separately according to males and females. Weight linear regression and weight logistic regression analyses were used to analyze the association between the monocyte-to-HDL-C ratio and BMD and osteopenia and osteoporosis and vertebral fracture. A total of 1804 participants were included. Among the participants with osteopenia, 398 (48.31%) were males and 466 (51.91%) were females. Among those with osteoporosis, 38 (2.77%) were males and 95 (9.50%) were females. In females, monocyte-to-HDL-C ratio was negatively associated with femoral neck BMD (regression coefficient (β) = -0.18; 95% confidence interval (CI): (-0.29, -0.07)) and high monocyte-to-HDL-C ratio was associated with higher odds of osteopenia (odds ratio (OR) = 1.22; 95% CI: (1.01, 1.47)) and osteoporosis (OR = 1.68; 95% CI: (1.13, 2.49)) after adjusting for confounders. In males, only monocyte-to-HDL-C ratio >0.35 was observed to be associated with higher odds of osteoporosis (OR = 1.96; 95% CI: (1.02, 3.79)). Stratified analyses showed that similar results were also found in different populations. This study showed that the monocyte-to-HDL-C ratio was negatively associated with BMD and the risk of osteopenia and osteoporosis in females. The monocyte-to-HDL-C ratio may be a new marker of osteoporosis or osteopenia.

Evaluating the long-term geomorphic process in-relation to hydrodynamics in the central coastal zone of Bangladesh

The central coast of Bangladesh is dynamic for its geographical location, hydrodynamic characteristics and residual flow. The research employed the Digital Shoreline Analysis System (DSAS), an ArcGIS extension tool, to conduct a historical trend analysis of shoreline. The study demonstrates that the central coast is eroding to the north and accreting to the south. The highest accretion value was found as 195.42 m/year, whereas the maximum value of erosion was estimated as −185.83 m/year, according to End Point Rate (EPR). The Linear Regression Rate (LRR) indicates that the average rate of erosion and accretion are −17.77 m/year and 17.88 m/year, respectively. Meanwhile, using Weighted Linear Regression (WLR), 0.48% of all transects demonstrated statistically significant erosion, while 0.43% showed statistically significant accretion. During the wet season, heavy river discharge leads to a low salt level in the ocean. Ocean currents hit central coast of Bangladesh from east to west, affecting the majority of the islands in the Meghna Estuary in the dry season. Changes in current directions can be seen during the wet seasons. Southern central coast areas are hit by south-east currents that split in two directions. The Sandwip Channel has a flow of 10,000 to 15,000 m³ s−1 northward. The Tetulia River, Shahbazpur Channel, and Hatia-Sandwip flow southward at rates ranging from 3000 to 17,000 m³ s−1, 14,000 to 60,000 m³ s−1, and 7000 to 39,000 m³ s−1, respectively. In the Meghna Estuary, the combined forces result in a counter-clockwise residual circulation, with the northward flow in the Sandwip channel and southbound flow in the Hatia and Shahbazpur channels. As a result of hydrodynamic, ocean currents, and residual flow, the Central Coast of Bangladesh is continually changing in appearance.

Assessment and Forecast of Shoreline Change Using Geo-Spatial Techniques in the Gulf of California

In coastal regions, the combined effects of natural processes, human activity, and climate change have caused shoreline changes that may increase in the future. The assessment of these changes is essential for forecasting their future position for proper management. In this context, shoreline changes in the Gulf of California (GC), Mexico, have received little attention and no previous studies have addressed future forecasting. In this study, the researchers assessed the historical shoreline changes to forecast the long-term shoreline positions. To address this, shoreline data were obtained from Landsat satellite images for the years 1981, 1993, 2004, 2010, and 2020. The Net Shoreline Movement (NSM), Linear Regression Rate (LRR), End Point Rate (EPR), and Weighted Linear Regression (WLR) geo-spatial techniques were applied to estimate the shoreline change rate by using a Digital Shoreline Analysis System (DSAS) in the GIS environment. A Kalman filter model was used to forecast the position of the shoreline for the years 2030 and 2050. The results show that approximately 72% of the GC shoreline is undergoing steady erosion, and this trend is continuing in the future. This study has provided valuable and comprehensive baseline information on the state of the shoreline in the GC that can guide coastal engineers, coastal managers, and policymakers in Mexico to manage the risk. It also provides both long-term and large-scale continuous datasets that are essential for future studies focused on improving the shoreline forecast models.

A quantitative analysis of multi-decadal shoreline changes along the East Coast of South Korea

South Korea's east coast is facing several issues related to coastal erosion because of sea-level rise, typhoon-induced storm surges, and various coastal development projects. In recent decades, high storm waves have frequently appeared on the east coast, causing casualty, beach erosion, and coastal infrastructure damage, drawing significant public attention. Thus, we analyzed the multi-decadal shoreline changes to understand the coastal dynamics and the forces responsible for the spatio-temporal changes along the 173 km coastline. The shorelines covering 38 years between 1984 and 2022 were derived from Landsat images and the change statistics, i.e., linear regression rate (LRR), endpoint rate (EPR), weighted linear regression (WLR), and net shoreline movement (NSM), were calculated at a 100 m alongshore intervals using Digital Shoreline Analysis System (DSAS), revealed several distinct behaviors of shoreline position. The long-period (1984–2022) assessment showed an average shoreline change rate (LRR) of 0.17 m/year with an estimated mean erosion and deposition rate of −0.57 and 2.07 m/year, respectively. The long-term surface gain and loss of the backshore region exhibited that the net surface gain of the east coast is 421.13 ha, and the net loss is 181.82 ha. The assessment of decadal shoreline changes showed a cyclic pattern of erosion (from 1984–1990 and 1999–2010) and accretion (from 1990–1999 and 2010–2022). Furthermore, a secondary level of investigation was conducted to address a wider variety of coastal behaviors by segmenting shoreline change rates based on coast types and average slopes along coastlines. It was observed that the frequent coastal deformation is associated with a flatter beach compared to a steep one. This study found that the artificial structures constructed along the east coast have not completely solved or stopped the erosion issues but shifted it from one location to another. The analysis of local and regional shoreline changes had shown that typhoon-induced storm surges, high storm waves, and anthropogenic activities like encroachment and the development of artificial coastal structures were the primary drivers of coastline changes along the east coast. Finally, we proposed a decision-making classification scheme that can be used to determine the mechanism of decision for protective and preventive measures against further coastal deterioration.

Virtual image-based cloud removal for Landsat images

ABSTRACT The inevitable thick cloud contamination in Landsat images has severely limited the usability and applications of these images. Developing cloud removal algorithms has been a hot research topic in recent years. Many previous algorithms used one or multiple cloud-free image(s) in the same area acquired on other date(s) as reference image(s) to reconstruct missing pixel values. However, it remains challenging to determine the optimal reference image(s). In addition, abrupt land cover change can substantially degrade the reconstruction accuracies. To address these issues, we present a new cloud removal algorithm called Virtual Image-based Cloud Removal (VICR). For each cloud region, VICR reconstructs the missing surface reflectance by three steps: virtual image within cloud region construction based on time-series reference images, similar pixel selection using the newly proposed temporally weighted spectral distance (TWSD), and residual image estimation. By establishing two buffer zones around the cloud region, VICR allows automatic selection of the optimal set of time-series reference images. The effectiveness of VICR was validated at four testing sites with different landscapes (i.e. urban, croplands, and wetlands) and land change patterns (i.e. phenological change, abrupt change caused by flooding and tidal inundation), and the performances were compared with mNSPI (modified neighborhood similar pixel interpolator), WLR (weighted linear regression) and ARRC (AutoRegression to Remove Clouds). Experimental results showed that VICR outperformed the other algorithms and achieved higher Correlation Coefficients and lower Root Mean Square Errors in surface reflectance estimation at the four sites. The improvement is particularly noticeable at the sites with abrupt land change. By considering the difference in the contributions from the reference images, TWSD can select more reliable similar pixels to improve the prediction of abrupt change in surface reflectance. Moreover, VICR is more robust to different cloud sizes and to changing reference images. VICR is also computationally much faster than ARRC. The framework for time-series image cloud removal by VICR has great potential to be applied for large datasets processing.

A Depth-Adaptive Filtering Method for Effective GPR Tree Roots Detection in Tropical Area

This study presents a technique for processing Step-frequency continuous wave (SFCW) ground penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution trade-offs. However, the high-frequency components of the transmission band suffers from poor penetrating capability and generates noise that interferes with root detection. The proposed time-frequency filtering technique uses a short-time Fourier transform (STFT) to track changes in frequency spectrum density over time. To obtain the filter window, a weighted linear regression (WLR) method is used. By adopting a conversion method that is a variant of the chirp Z-Transform (CZT), the time-frequency window filters out frequency samples that are not of interest when doing the frequency-to-time domain data conversion. The proposed depth-adaptive filter window can self-adjust to different scenarios, making it independent of soil information and effectively determines subsurface tree roots. The technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, and the two-dimensional (2D) radar map of subsurface root systems is highly improved compared to existing methods.

Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server. Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics. The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods.

Abstract 10664: Association of Unfavorable Social Determinants of Health With Stroke/TIA and Vascular Risk Factors: Results From the Hispanic Community Health Study/Study of Latinos

Introduction: Social Determinants of Health (SDOH) contribute to the development of diseases and disproportionally affect minoritized groups. There is a paucity of information describing the relationship between SDOH, stroke/TIA and vascular risk factors (VRFs) in Hispanics/Latinos. Hypothesis: Unfavorable SDOH are associated with self-reported stroke/TIA and with increased VRFs. Methods: Cross-sectional data from cohort study of Hispanic Community Health Study/Study of Latinos (2014-2017). SDOH were assessed using questionnaires and previously validated scales. VRFs were hypertension, diabetes mellitus, hypercholesterolemia, smoking, at-risk alcohol use, and obesity. We investigated the association between the SDOH (individually and as count) and stroke/TIA using weighted complex survey multivariable logistic and linear regression analyses. Results: For individuals with stroke/TIA (weighted n=388), the mean age (58.9, 95% CI=56.4-61.5) differed from those without stroke/TIA (weighted n=11,210; 46.8, 95% CI=46.3-47.4). In bivariate analysis, income <$20,000, education less than high school, no health insurance, perceived discrimination, not retired and not currently employed, upper tertile for chronic stress, and lower tertiles for social support and language- and social-based acculturation were associated with stroke/TIA and used in further analysis. Participants were grouped into 5 categories based on the distribution of SDOH (≤1, 2, 3, 4 and ≥5 SDOH). Increasing number of SDOH was directly associated with the odds of each individual VRFs investigated (except for at-risk alcohol) and VRF count (β=0.11, 95% CI=0.09-0.14). After adjustment for confounders, the odds of stroke/TIA increased 2.3 times in individuals with three SDOH (95% CI 1.6-3.2) and 2.7 times (95% CI 1.9-3.7) for those with ≥5 SDOH, compared to those with ≤1 SDOH. In final model, income, discrimination, social support, chronic stress, and employment status were individually associated with higher odds of stroke/TIA. Conclusions: Increased number of SDOH increased odds for stroke/TIA and VRFs. The association between SDOH and stroke/TIA remained significant after adjustment for VRFs, suggesting the participation of additional mechanisms of disease.

Long-term shoreline evolution (Late-Holocene – 2018) of the Channel 15 coast (Samborombón bay, NE – Buenos Aires province, Argentina)

Here we present an investigation with the aim of reconstructing the shoreline evolution and geomorphological changes in the central zone of Samborombón bay (Channel 15, Buenos Aires province, Argentina) during the period 2700 cal yr BP to recent times. Palynological, geomorphological and historical imagery and proxy-data analyses were combined to reconstruct the shoreline evolution and paleo-environmental changes. Palynological material (pollen and non-pollen palynomorphs -NPP-) was recovered from a continuous 117 cm long sedimentary profile extracted from an outcrop at the northern margin of Channel 15. Relict landforms, including paleo- shorelines, were reconstructed by applying enhancing filters to a Spot 6 image from 2015. Historical shorelines were identified in high-definition images (aerial and satellite imagery) between 1968 and 2018, but also by using the marsh vegetation edge as a shoreline proxy. Shoreline change was quantified using the Net Shoreline Movement and Weighted Linear Regression algorithms in ArcMap 10.1. Results based on the palynological analysis for the period 2700–2740 BP, show the passage from subtidal-intertidal to supratidal environment with influence of brackish to fresh-waters, where paleo-shorelines imprint was recognized developing over a tidal plain that was grading to marshes. Results based on aerial and satellite imagery for the period 1968–2018 show shoreline retrocession only immediately at the north of the channel mouth, while the rest of the coast prograded toward the estuary. These processes indicate that new marshes – and, therefore, wetlands – are constantly developing at the edge of Samborombón bay.

Response of long- to short-term tidal inlet morphodynamics on the ecological ramification of Chilika lake, the tropical Ramsar wetland in India

The long- to short-term morphodynamic response in low-lying coastal wetlands raises serious concerns worldwide about the loss of their biodiversity and ecological ramifications due to change in tidal amplitude and cyclonic events. One such place worth studying is Chilika lake, India, a prominent Ramsar site, the largest brackish water lagoon in Asia, and the second-largest coastal lagoon in the world. It experiences frequent cyclone landfalls and strong littoral drift that tends to open/close the tidal inlet. The goal of this study was to analyze the response of slow onset events such as long- (1952–2020) to short-term (~annual scale from 1989 to 2020) tidal inlet movement, shoreline change (1990–2020 with almost every five-year interval), spit morphodynamics (~annual scale from 1989 to 2020) on ecological ramification in Chilika lake as well as the implications of sudden onset event such as cyclonic landfall. In this study, we used the Digital Shoreline Change Analysis System (DSAS) to compute the statistics of shoreline change rate by calculating end point rate (EPR) values for short-term shoreline change (1990, 1995, 2000, 2005, 2011, 2016, and 2020) and weighted linear regression (WLR) for long-term shoreline change (1990–2020). The results show that Chilika lake experienced both erosion and accretion processes with a remarkably high erosion rate of 19.87 m year−1 and accretion of 16.91 m year−1 during a long-term scale (1990–2020). The average erosion and accretion rates were 2.25 m year−1 and 4.67 m year−1, respectively, during the past three decades (1990–2020). The short-term analysis suggests that the highest mean erosion of 4.37 m year−1 occurred during 2005–2011, mainly due to cyclonic storms, reduction in sediment discharge, and lunar eclipse, which induced tide with very high amplitude in August 2008. Overall, the annual scale analysis of tidal inlet shows a shifting trend towards the northward side even after the artificial opening of an inlet in 2000. It can be ascribed mainly to the prevalent direction of longshore drift along this coast. This study observed that the landfall of cyclones significantly affects the spit morphodynamics and opening of the tidal inlet, which defines the inflow of the seawater into the lagoon and further substantial impacts on the ecological ramification. The current study's methodology can be extended to comprehend the response of long- to short-term changes of the tidal inlet, shoreline, and spit morphodynamics on the ecological ramification of coastal lagoons worldwide along with impacts of sudden-onset events caused by cyclonic landfall.

Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode.

This work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy, precision, and robustness were validated. The normality of the experimental data was tested and confirmed for both methods. Heteroscedasticity was checked by residual analysis followed by a statistical F-test. The latter was confirmed for both analytes. The low relative standard deviations (RSD) at all calibration points and repetitive slopes justified the use of a calibration curve; therefore, the standard addition methodology was avoided (the latter is common in electroanalysis, but time-consuming). Since the conditions for using an ordinary least squares (OLS) regression were not met, weighted linear regression (WLR) was used to improve the accuracy of the analytical results at low concentrations of the analytes. In this manner, the best weighted model was determined and used for the quantification. A comparison was made between the OLS and WLR methods to show the necessity of using the WLR method for EP and UA analysis. The newly developed and validated methods were also shown to be effective in the analysis of real samples. The content of EP in an EP auto-injector and UA in human urine was tested by employing the best weighted model. For EP and UA, the accuracy in terms of the average recovery value was 101.01% and 94.35%, and precision in terms of RSD was 5.65% and 2.75%, respectively. A new analytical methodology is presented that uses a low volume (a single drop), and it offers the advantage of electroanalysis for on-site analysis, where conventional chromatographic techniques cannot be easily employed. Furthermore, the developed technique has additional advantages in terms of speed, cost, and miniaturization.

Evolution of southern Mexican Pacific coastline: Responses to meteo-oceanographic and physiographic conditions

The coast of Oaxaca, along the southern Mexican Pacific coastline, is home to diverse ecosystems, which undergoes significant erosion and accretion due to natural and anthropogenic influences. This study aims in analysing the historical shoreline variations and predicting the future changes for better coastal management plans. In order to study the long-term variations in shoreline, Landsat time-series satellite imageries between 1973 and 2020 were evaluated. Overall, the results indicate that the coast is highly dominated by accretion (74%) process, with the most significant changes occurring at the estuarine areas and beaches. Linear Regression Rate (LRR) and Weighted Linear Regression (WLR) methods showed mean variations at rates of 1.2±2.5 and 0.4±2.5 m/yr for the long-term period. The analysis of seven short-term periods (for every 5 years between 1986–2020) identified the maximum mean erosion of −20.6 m during 2005–2010 and accretion of 18.7 m during 2000–2005. However, the forecasted rate of change for the period 2030 and 2040 using End Point Rate (EPR) statistics revealed that the northern region would continue to move seaward at a rate of +0.26 m/yr and the coastline of southern region would move landward at a possible rate of −0.45 m/yr. The natural factors such as rainfall, river discharge, hurricane, and wave dynamics are the main drivers of shoreline changes along the estuarine and wetland areas. Contrarily, the anthropogenic factors such as breakwaters and urbanization are responsible for coastline dynamics along the beaches and ports. Henceforth, the estuarine region, lagoon openings, and tourist beaches require utmost attention as the sediment flow in these regions are not monitored constantly.

Machine learning techniques for liquid level estimation using FBG temperature sensor array

This paper proposes the use of the fiber Bragg grating (FBG) temperature sensors array to estimate the fluid level. The tank is 100 cm in height and 30 cm in width, with 9 FBG sensors distributed along with the tank height. This work proposes level estimation in two steps: level detection (classification) and level estimation (regression). The level detection consists of finding under which FBG the level is. We dichotomize the classes in water and not water: air. For the detection, we use the following Machine Learning (ML) algorithms: Logistic Regression (LogR), Decision Tree (DT), and Support Vector Machine (SVM). We chose the algorithms based on their usability in literature and theoretical consolidation. The algorithm with the best results among the ones tested is DT, resulting in an average accuracy of 89.54%. For the level estimation, we use the wavelength shift in conjunction with the classification obtained via DT. The level estimation consists of estimating in cm the location of the level. The algorithms chosen are Weighted Linear Regression (WLR), Support Vector Regression (SVR), SVR with kernel selection minimize cost (SVRmin). Each of the applied algorithms presented better results in different level intervals. Thus, we propose the Mixed Model (MM), which selects the lowest Root Mean Square Error (RMSE) among the tested regression algorithms at each level and associates it with it. The MM has a RMSE of 3.56 cm, which is approximately four times smaller than when using WLR. The SVM and SVMmin have RMSE of 6.28 cm and 6.14 cm, respectively.

Coastal system evolution along the southeastern Caspian Sea coast using satellite image analysis: response to the sea level fall during 1994–2015

The southeastern segment of the Caspian Sea (CS) along the Iranian coast, which has a very gentle inner shelf slope, underwent rapid beach evolution in response to water level changes. Based on satellite altimetry data, the CS basin has experienced an accelerated water level fall drop in recent years, leading to an increase in the rate of shoreline changes. The shoreline dynamics and morphodynamics of the Amirabad coastal zone, Gorgan Bay, Miankaleh Spit, and Gomishan Lagoon are evaluated here to determine morphological changes of the study area. Landsat-TM/ETM+/OLI moderate spatial resolution products were acquired at unequal intervals between 1994 and 2015 to monitor the coastal changes using Geographic Information System (GIS) tools. The Digital Shoreline Analysis System (DSAS), a GIS software extension, has been used for estimation of the shoreline rate of change through several approaches, including End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR). The results show that both Gorgan Bay and Gomishan Lagoon have significantly decreased in size during recent years, mainly due to the fall of the CS water level. Results also show that water level drop of the CS, along with net eastward littoral transport, resulted in the growing of the Miankaleh Spit. Further analysis based on the wave climate in the southeast CS and net estimated longshore sediment transport indicates that the littoral drift contributed to ~ 6% of the total annual elongation of the Miankaleh Spit during the study period.

Estimating parking generation rate for Karbala holy city using multi-variables approach

Car parking planning, design, and management processes are very important to all cities and places to ensure efficient traffic system. Estimating the demand of car parking represents the significant start point for the success of these processes. Generally, there are many local and international estimating criteria, but such criteria need continuing update due to many reasons related to socioeconomic factors, lifestyle changes, development in technology, etc. Moreover, the majority of these criteria depend on single parameter for the estimation of parking demand; such as bed or employee for hospital, gross floor area or employee for office, and so on. The main aim of this research is to estimate the park generation rate for specific land uses depend on multivariable to increase the accuracy and limiting the effect of variation in parameters. Statistical analysis was conducted to create predicting models for each land use. The collected data was nominated for Karbala holy city, where different parameters are scaled for different city sectors. Groups of statistical models (i.e., simple, multi linear and nonlinear statistical models, and Weighted Linear Regression (WLR)) were used to create best representative relationship between the number of demands for car parking and multivariable parameters or factors affecting these lands used demands. Resulted statistical models were tested for best fit using statistical indices for model verification. Results disclose the significant of multivariable model compare with simple models. Also, WLR model shows it validity compare with multi-regression model for almost land use models. Consequently, for more accurate estimation the multi variable models are initiated with continuous need for updating.

Temporal unmixing-based cloud removal algorithm for optically complex water images

ABSTRACT Remote sensing has become an essential technique in water environment monitoring. However, the cloud contamination of remote sensing images breaks its spatial and temporal continuity in practical application. The current cloud removal algorithms always based on the hypothesis that the ground surface has no significant changes temporally, which is not proper for optically complex water body. Therefore, to overcome the weakness of current cloud removal algorithms on temporally fast-changing landscapes, a temporal unmixing-based cloud removal (TUBCR) algorithm is proposed to estimate the under-cloud information of optically complex water images. In this algorithm, the spatial information and temporal information were separated into temporal abundance images and temporal endmembers. By extracting the high temporal resolution endmembers, the fast-changing water information were estimated. The performance of the proposed algorithm was compared with those of two widely used cloud removal algorithms: the modified neighbourhood similar pixel interpolator (mNSPI) algorithm and the weighted linear regression (WLR) algorithm. The results of four simulated datasets (500 images in total) and two real Geostationary Ocean Colour Imager (GOCI) datasets (8 images in total) show that the proposed algorithm performed better in both quantitative indexes and visual effect compared with the mNSPI algorithm and the WLR algorithm. In satellite image time series, the proposed algorithm could accurately estimate under-cloud water information and correct the offsets of the statistical temporal trends that caused by cloud contamination. Hence, theoretically, the proposed TUBCR algorithm altered the similar pixel-based strategy of classic methods and got a satisfied performance in fast-changing water images. In applications, the TUBCR algorithm has great potential to improve the spatiotemporal coverage of optically complex water remote sensing.

Analyzing shoreline dynamicity and the associated socioecological risk along the Southern Odisha Coast of India using remote sensing-based and statistical approaches

The coastal zone is an extremely volatile environment and is constantly changing. We assessed short- and long-term shoreline changes in the Ganjam district of Odisha on the eastern coast of India from 1990 to 2019 using Landsat satellite imagery and the Digital Shoreline Analysis System (DSAS) tool in a geographic information system. In addition, we have also projected the likely future coastline position for the 2030–2040 period and the possible impact on the socioecology of the shoreline. In this study, we used the endpoint rate (EPR) analysis, weighted linear regression (WLR) analysis, and trigonometric functions to analyze the shoreline from 1990 to 2019 and also forecasted for year 2030 and 2040. The shoreline of the Ganjam coastal zone is one of the most biologically productive ecosystems in the world, and it is well-known due to the breeding and mass nesting grounds of olive ridley turtles and the economically connected ports, famous beaches, and cyclone-prone areas. During the study period (1990–2019), the average erosion and accretion rates in the Ganjam shoreline were −2.58 m/year and 11.63 m/year, respectively. The rate of shoreline erosion increased during years of cyclone landfall, which was revealed during the short-term shoreline analysis of the periods from 1995 to 2000 (1999 super cyclone) and 2015 to 2019 (2019 category – IV cyclone Fani). The short- to long-term analyses of the shoreline assisted in identifying erosion (Ramyapatna, Podampetta) and accretion (southern part of Gopalpur port, spits along the Bahuda and Rushikulya Rivers) hotspots within the Ganjam coastal zone. The identified erosion hotspots could submerge a significant number of coastal villages that serve as breeding and mass nesting grounds for olive ridley turtles. The dominant erosion along the Ganjam coastline are likely to enhance socioecological risk and further threaten coastal communities in the future. The output of the undertaken research will benefit coastal planners, policymakers, and conservationists by helping them to formulate the most suitable action plan for coastal zone management with consideration of all stakeholders.

Evaluation of decadal shoreline changes along the Parnaíba Delta (NE Brazil) using satellite images and statistical methods

This study investigated changes of the shoreline position in the Parnaíba River Delta (PRD), which is in an underdeveloped and sparsely populated area. The general behavior of the shoreline between 1984 and 2017 was analyzed using 12 Landsat satellite images and the Digital Shoreline Analysis System (DSAS) software. DSAS applied statistical methods, such as Weighted Linear Regression (WLR) and End Point Rate (EPR), to define whether the shoreline was retreating or accreting.Data analysis revealed that, on a decadal time scale (33 years), 52% of the PRD shoreline retreated under erosional conditions, while 48% accreted. Accretion mainly occurred at the vicinities of the river mouth, especially eastward where the highest progradation rate was observed (maximum of 29 m/year). Shoreline retreat, up to 9 m/year, occurred predominantly in the western part of the PRD and away from the river mouth.However, changes of the general behavior of the shoreline (retreat/accretion) were observed at an interdecadal time scale, which were positively related to variations in river discharge. Variations of river discharge are directly dependent on the rainfall amount along the drainage basin that, in this region, is controlled by climatic conditions such as El Niño and La Niña. Therefore, the reduced river discharge observed between 1995-1999 and 2009–2012 (El Niño periods), was the major cause the triggered the observed coastal retreat (>6 m/year) on PRD shoreline during these periods.Consequently, the variability of natural factors, such as river discharge and rainfall, are the main drivers of shoreline changes on PRD, since anthropogenic impacts have hitherto only marginal effects on the delta region and on the drainage basin of the Parnaíba river.

Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil

The coastal area of João Pessoa city, Paraíba, Brazil, is densely populated and has a large flow of trade and services. More recently, this region has been suffering from the advance of the sea, which has caused changes in the shoreline and caused a decrease in the beach area and damage to various urban facilities. Thus, the spatiotemporal changes of the short- and long-term characteristics of the shoreline of João Pessoa city over the past 34 years (1985–2019) were calculated and the forcing mechanisms responsible for the shoreline changes were analyzed. Remote sensing data (Landsat 5-TM and 8-OLI) and statistical techniques, such as endpoint rate (EPR), linear regression rate (LRR) and weighted linear regression (WLR), using Digital Shoreline Analysis System (DSAS), were used. In this study, 351 transects ranging from ~1.1 km to ~6 km were analyzed within four zones (Zones I to IV), and the main controlling factors that influence the shoreline changes in these zones, such as sea level, tidal range, wave height, beach morphology and ocean currents, were discussed. The long-term change from 1985 to 2019 showed primarily accretion on the shoreline of João Pessoa city, with the rate of 0.55 m/year (WLR method); 282 transects showed accretion. The results showed that Zone-I, which was located in the south of the study area, was the only zone that primarily recorded erosion from 1985 to 2019, with a mean rate of −0.23 m/year according to the WLR method. According to the short-term shoreline change analysis, a cyclical pattern of erosion was observed in the 1985–1990, 1993–1999, 2005–2011 and 2014–2019 periods, and accretion was observed in the 1990–1993, 1999–2005 and 2011–2014 periods. It was inferred that the patterns of all analyzed zones were similar, i.e., when majority accretion was detected within a zone, the other zones also recorded accretion, except for during the period from 2014 to 2019, when Zone-I showed different behavior. Finally, the long- and short-term analyses showed that the coastal area of João Pessoa city is influenced by various forcing mechanism responsible for the shoreline changes.