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Significant subsidence is susceptible to groundwater level variations in aquifer systems. The relation between groundwater level change and global positioning system (GPS) estimated subsidence is spatially variable. Time-dependent spatial regression can be used for the estimation of groundwater level changes using GPS based deformation data. Furthermore, the model can be validated using observed hydraulic head data from available monitoring stations. This study uses GPS station data to estimate the monthly groundwater levels in the west-central Taiwan for the period: 2016–17. Time-dependent spatial regression provides a more realistic estimation of groundwater level changes in response to highly heterogeneous aquifer properties than other methods. The high correlation (r = 0.95) between observed and estimated groundwater levels shows that GPS estimated deformations represent an alternative approach for estimating seasonal groundwater changes. Due to availability of spatially broad/low cost GPS data (compared to the sparse availability groundwater monitoring stations), the use of GPS data represents a powerful solution for future monitoring of estimated seasonal groundwater level changes in areas where only few groundwater observations are available.
Muhammad Zeeshan Ali; Hone-Jay Chu; Tatas; Thomas J. Burbey. Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation. Environmental Modelling & Software 2021, 143, 105123 .
AMA StyleMuhammad Zeeshan Ali, Hone-Jay Chu, Tatas, Thomas J. Burbey. Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation. Environmental Modelling & Software. 2021; 143 ():105123.
Chicago/Turabian StyleMuhammad Zeeshan Ali; Hone-Jay Chu; Tatas; Thomas J. Burbey. 2021. "Spatio-temporal estimation of monthly groundwater levels from GPS-based land deformation." Environmental Modelling & Software 143, no. : 105123.
Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m−3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m−3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.
Hone-Jay Chu; Yu-Chen He; Wachidatin Chusnah; Lalu Jaelani; Chih-Hua Chang. Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression. Sustainability 2021, 13, 6416 .
AMA StyleHone-Jay Chu, Yu-Chen He, Wachidatin Chusnah, Lalu Jaelani, Chih-Hua Chang. Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression. Sustainability. 2021; 13 (11):6416.
Chicago/Turabian StyleHone-Jay Chu; Yu-Chen He; Wachidatin Chusnah; Lalu Jaelani; Chih-Hua Chang. 2021. "Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression." Sustainability 13, no. 11: 6416.
Choshui River alluvial fan, Taiwan. Land subsidence caused by groundwater overexploitation is a critical global problem. The spatial distribution of land subsidence is crucial for effective environmental management and land planning in subsidence prone areas. Because of the nonlinear relationship between subsidence and drawdown due to groundwater exploitation in heterogeneous aquifers, a spatial regression (SR) model is developed to effectively estimate nonlinear and spatially varying land subsidence. Considering various data inputs in the Choshui River alluvial fan, the SR model offers a robust method for accurately estimating the spatial patterns of subsidence using only drawdown as input data. Without requiring extensive calibration or an elaborate numerical groundwater flow and subsidence model, the model provides annual subsidence patterns using a spatially varying relationship between drawdown and resulting land subsidence. Results show that the largest water-level cone of depression occurs in the distal fan area. Nonetheless, the calculated subsidence bowl closely approximates the observed one located much farther inland. The root-mean-square-errors (RMSEs) of annual subsidence is less or equal to 0.76 cm for the SR. Results indicate that the SR model reasonably estimates the spatial distribution of the skeletal storage coefficient in the aquifer system. The large coefficient that represents high potential of inelastic compaction occurs in the southern inland area, whereas the small coefficient that represents elastic compaction occurs in the northern area and proximal fan. Furthermore, this method can be used efficiently for subsidence management/ regulation and might be widely used for subsidence estimation solely based on drawdown.
Hone-Jay Chu; Muhammad Zeeshan Ali; Tatas; Thomas J. Burbey. Development of spatially varying groundwater-drawdown functions for land subsidence estimation. Journal of Hydrology: Regional Studies 2021, 35, 100808 .
AMA StyleHone-Jay Chu, Muhammad Zeeshan Ali, Tatas, Thomas J. Burbey. Development of spatially varying groundwater-drawdown functions for land subsidence estimation. Journal of Hydrology: Regional Studies. 2021; 35 ():100808.
Chicago/Turabian StyleHone-Jay Chu; Muhammad Zeeshan Ali; Tatas; Thomas J. Burbey. 2021. "Development of spatially varying groundwater-drawdown functions for land subsidence estimation." Journal of Hydrology: Regional Studies 35, no. : 100808.
Landslides are one of the most devastating natural hazards worldwide. Landslides are triggered by different forces, such as earthquakes and typhoons, and have different characteristics in terms of distribution, influential factors, and process. The objectives of this study are to develop susceptibility maps using machine learning for two different triggering forces (earthquake and typhoon) and identify the main predisposing factors in mountainous regions of Pakistan and Taiwan. To compare different machine learning models for landslide susceptibility mapping, landslide susceptibility maps were developed using traditional (logistic regression) and modern techniques (decision tree). Results show that the spatial pattern of susceptibility map from logistic regression is continuously distributed, whereas that from the decision tree is crisp and sharp. From both models, consistent results show that the most important critical factors are completely different for both the earthquake- and typhoon-triggered landslides. For rainfall-triggered landslides in Taiwan, the most important factor of landslide susceptibility is the distance to the rivers, whereas, for earthquake-triggered landslides in Pakistan, the most important one is geological formations. Moreover, landslide susceptibility maps show that earthquake-triggered landslides tend to occur at the Muzaffarabad Formation, whereas rainstorm-induced landslides aggregate in the slope toe along the river.
Muhammad Zeeshan Ali; Hone-Jay Chu; Yi-Chin Chen; Saleem Ullah. Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification. Environmental Earth Sciences 2021, 80, 1 -21.
AMA StyleMuhammad Zeeshan Ali, Hone-Jay Chu, Yi-Chin Chen, Saleem Ullah. Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification. Environmental Earth Sciences. 2021; 80 (6):1-21.
Chicago/Turabian StyleMuhammad Zeeshan Ali; Hone-Jay Chu; Yi-Chin Chen; Saleem Ullah. 2021. "Machine learning in earthquake- and typhoon-triggered landslide susceptibility mapping and critical factor identification." Environmental Earth Sciences 80, no. 6: 1-21.
Land subsidence provides important information about the spatial and temporal changes occurring in the subsurface (e.g. groundwater levels, geology, etc.). However, sufficient subsidence data are difficult to obtain using only one sensor or survey, often resulting in a tradeoff between spatial resolution and temporal coverage. This study aims to estimate the high spatio-temporal resolution land subsidence by using a kernel-based vector data fusion approach between annual leveling and monthly subsidence monitoring well data, while invoking an invariant relation of subsidence information. Subsidence patterns and processes can be identified when spatio-temporal fusion of sensor data are implemented. In this subsidence investigation in Yunlin and Chunghua counties, Taiwan, the root mean square error (RMSE) is 0.52 cm in the fusion stage, and the mapping RMSE is 0.53 cm in the interpolation. The fused subsidence data readily show that the subsidence hotspot varies with time and space. The subsidence hotspots are in the western region during the winter (related to aquaculture activities) but move to the inland areas of Yunlin County during the following spring (related to agricultural activities). The proposed approach can help explain the spatio-temporal variability of the subsidence pattern.
Hone-Jay Chu; Muhammad Zeeshan Ali; Thomas J. Burbey. Spatio-temporal data fusion for fine-resolution subsidence estimation. Environmental Modelling & Software 2021, 137, 104975 .
AMA StyleHone-Jay Chu, Muhammad Zeeshan Ali, Thomas J. Burbey. Spatio-temporal data fusion for fine-resolution subsidence estimation. Environmental Modelling & Software. 2021; 137 ():104975.
Chicago/Turabian StyleHone-Jay Chu; Muhammad Zeeshan Ali; Thomas J. Burbey. 2021. "Spatio-temporal data fusion for fine-resolution subsidence estimation." Environmental Modelling & Software 137, no. : 104975.
The data quality of low-cost sensors has received considerable attention and has also led to PM2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.
Hone-Jay Chu; Muhammad Zeeshan Ali; Yu-Chen He. Spatial calibration and PM2.5 mapping of low-cost air quality sensors. Scientific Reports 2020, 10, 1 -11.
AMA StyleHone-Jay Chu, Muhammad Zeeshan Ali, Yu-Chen He. Spatial calibration and PM2.5 mapping of low-cost air quality sensors. Scientific Reports. 2020; 10 (1):1-11.
Chicago/Turabian StyleHone-Jay Chu; Muhammad Zeeshan Ali; Yu-Chen He. 2020. "Spatial calibration and PM2.5 mapping of low-cost air quality sensors." Scientific Reports 10, no. 1: 1-11.
The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.
Adindha Surya Anugraha; Hone-Jay Chu; Muhammad Zeeshan Ali. Social Sensing for Urban Land Use Identification. ISPRS International Journal of Geo-Information 2020, 9, 550 .
AMA StyleAdindha Surya Anugraha, Hone-Jay Chu, Muhammad Zeeshan Ali. Social Sensing for Urban Land Use Identification. ISPRS International Journal of Geo-Information. 2020; 9 (9):550.
Chicago/Turabian StyleAdindha Surya Anugraha; Hone-Jay Chu; Muhammad Zeeshan Ali. 2020. "Social Sensing for Urban Land Use Identification." ISPRS International Journal of Geo-Information 9, no. 9: 550.
Many regions of the earth are experiencing land subsidence owing to aquifer-system compaction, a consequence of groundwater depletion manifesting as excessive groundwater drawdown. The relation between groundwater drawdown and land subsidence caused by aquifer-system compaction is nonstationary in space and time due to the highly heterogeneous aquifer material, hydraulic and mechanical properties, and spatio-temporal variations in aquifer recharge and groundwater extraction. Annual land subsidence maps are developed using geographical time-slice weighted regression (GTSWR) and geographical temporal weighted regression (GTWR). Considering these spatiotemporal regressions, groundwater drawdown is used as the input parameter to estimate spatial and temporal patterns of land subsidence in both Changhua and Yunlin counties, Taiwan, for an 8-year period. Results indicate that the GTSWR or GTWR models yield greater accuracy with a lower root mean square error (RMSE) than linear regression (LR). The correlation between the predicted and observed data for LR, GTSWR and GTWR is 0.31, 0.93 and 0.94, respectively. In the spatiotemporal models, areas with smaller model coefficients represent over-consolidated sediments, whereas the areas with larger coefficients represent where sediments are normally consolidated. Normally consolidated sediments tend to produce the greatest amount of land subsidence. Annual subsidence patterns reveal that greater levels of subsidence are progressing inland. The greatest level of subsidence occurs in central Yunlin (7 cm/year) due to groundwater extraction. The spatio-temporal regression model is used to predict the effects of reduced groundwater extraction for different areas based on two scenarios of 30 and 50% reductions in groundwater drawdown.
Muhammad Zeeshan Ali; Hone-Jay Chu; Thomas J. Burbey. Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeology Journal 2020, 28, 2865 -2876.
AMA StyleMuhammad Zeeshan Ali, Hone-Jay Chu, Thomas J. Burbey. Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeology Journal. 2020; 28 (8):2865-2876.
Chicago/Turabian StyleMuhammad Zeeshan Ali; Hone-Jay Chu; Thomas J. Burbey. 2020. "Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations." Hydrogeology Journal 28, no. 8: 2865-2876.
An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya–Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water. Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.
Hone-Jay Chu; Lalu Muhamad Jaelani; Manh Van Nguyen; Chao-Hung Lin; Ariel C. Blanco. Spectral and spatial kernel water quality mapping. Environmental Monitoring and Assessment 2020, 192, 1 -12.
AMA StyleHone-Jay Chu, Lalu Muhamad Jaelani, Manh Van Nguyen, Chao-Hung Lin, Ariel C. Blanco. Spectral and spatial kernel water quality mapping. Environmental Monitoring and Assessment. 2020; 192 (5):1-12.
Chicago/Turabian StyleHone-Jay Chu; Lalu Muhamad Jaelani; Manh Van Nguyen; Chao-Hung Lin; Ariel C. Blanco. 2020. "Spectral and spatial kernel water quality mapping." Environmental Monitoring and Assessment 192, no. 5: 1-12.
Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10‐year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.
Hone‐Jay Chu; Cheng‐Wei Lin; Thomas J. Burbey; Muhammad Zeeshan Ali. Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption. Groundwater 2020, 58, 962 -972.
AMA StyleHone‐Jay Chu, Cheng‐Wei Lin, Thomas J. Burbey, Muhammad Zeeshan Ali. Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption. Groundwater. 2020; 58 (6):962-972.
Chicago/Turabian StyleHone‐Jay Chu; Cheng‐Wei Lin; Thomas J. Burbey; Muhammad Zeeshan Ali. 2020. "Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption." Groundwater 58, no. 6: 962-972.
Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration–duration–frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control.
Hone-Jay Chu; Muhammad Zeeshan Ali. Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM2.5. International Journal of Environmental Research and Public Health 2020, 17, 1419 .
AMA StyleHone-Jay Chu, Muhammad Zeeshan Ali. Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM2.5. International Journal of Environmental Research and Public Health. 2020; 17 (4):1419.
Chicago/Turabian StyleHone-Jay Chu; Muhammad Zeeshan Ali. 2020. "Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM2.5." International Journal of Environmental Research and Public Health 17, no. 4: 1419.
In the near future, multi-sensor fusion will be the core component to navigate the autonomous driving platforms. This paper proposes new strategies to cope with the integration of an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LIDAR) to achieve simultaneous localization and mapping (INS/GNSS/LiDAR SLAM) especially in GNSS challenging environments where GNSS signals are blocked or contaminated with reflected signals. The proposed strategies implement a high level of integration with various information received from multiple sensors to collectively compensate for the specific drawbacks of those sensors included in the integrated system. The first strategy is to solve the divergence and drift problems of SLAM using the initial pose information from INS and the proposed refreshing process using an INS/GNSS integrated system. In addition, an updated mechanization is designed to qualify those received measurements based on cross validation of separate types of data. This mechanization is to ensure all measurements are reliable for the Extended Kalman Filter (EKF) update process. Moreover, the SLAM-derived information plays a major role to recognize the vehicle movement which assists the system to accurately apply those appropriate vehicle motion constraint models. The preliminary results presented in this study illustrate that proposed algorithm performs superior than the traditional INS/GNSS integration scheme and provides absolute navigation accuracy of 2 meters and 0.6% of distance traveled in GNSS-denied as well as 1.2 meters in GNSS-hostile environments, respectively.
Kai-Wei Chiang; Guang-Je Tsai; Hone-Jay Chu; Naser El-Sheimy. Performance Enhancement of INS/GNSS/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy. IEEE Transactions on Vehicular Technology 2020, 69, 2463 -2476.
AMA StyleKai-Wei Chiang, Guang-Je Tsai, Hone-Jay Chu, Naser El-Sheimy. Performance Enhancement of INS/GNSS/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy. IEEE Transactions on Vehicular Technology. 2020; 69 (3):2463-2476.
Chicago/Turabian StyleKai-Wei Chiang; Guang-Je Tsai; Hone-Jay Chu; Naser El-Sheimy. 2020. "Performance Enhancement of INS/GNSS/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy." IEEE Transactions on Vehicular Technology 69, no. 3: 2463-2476.
The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 mg · m − 3 to 6.37 mg · m − 3 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- a improve from 0.56 to 0.89.
Manh Van Nguyen; Chao-Hung Lin; Hone-Jay Chu; Lalu Muhamad Jaelani; Muhammad Aldila Syariz. Spectral Feature Selection Optimization for Water Quality Estimation. International Journal of Environmental Research and Public Health 2019, 17, 272 .
AMA StyleManh Van Nguyen, Chao-Hung Lin, Hone-Jay Chu, Lalu Muhamad Jaelani, Muhammad Aldila Syariz. Spectral Feature Selection Optimization for Water Quality Estimation. International Journal of Environmental Research and Public Health. 2019; 17 (1):272.
Chicago/Turabian StyleManh Van Nguyen; Chao-Hung Lin; Hone-Jay Chu; Lalu Muhamad Jaelani; Muhammad Aldila Syariz. 2019. "Spectral Feature Selection Optimization for Water Quality Estimation." International Journal of Environmental Research and Public Health 17, no. 1: 272.
The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.
Hone-Jay Chu; Mạnh Van Nguyen; Lalu Muhamad Jaelani. Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal. Water Resources Management 2019, 34, 311 -325.
AMA StyleHone-Jay Chu, Mạnh Van Nguyen, Lalu Muhamad Jaelani. Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal. Water Resources Management. 2019; 34 (1):311-325.
Chicago/Turabian StyleHone-Jay Chu; Mạnh Van Nguyen; Lalu Muhamad Jaelani. 2019. "Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal." Water Resources Management 34, no. 1: 311-325.
Since the patterns of residential buildings in the urban area are small-sized and dispersed, this study proposes a high-resolution flood loss and risk assessment model to analyze the direct loss and risk impacts caused by floods. The flood inundation simulation with a fine digital elevation model (DEM) provides detailed estimations of flood-inundated areas and their corresponding inundation depths during the 2016 Typhoon Megi and 2017 Typhoon Haitang. The flood loss assessment identifies the impacts of both events on residential areas. The depth-damage table from surveys in the impacted area was applied. Results indicated that the flood simulation with the depth-damage table is an effective way to assess the direct loss of a flood disaster. The study also showed the effects of spatial resolution on the residential loss. The results indicated that the low-resolution model easily caused the estimated error of loss in dispersed residential areas when compared with the high-resolution model. The analytic hierarchy process (AHP), as a multi-criteria decision-making method, was used to identify the weight factor for each vulnerability factor. The flood-vulnerable area was mapped using natural and social vulnerability factors, such as high-resolution DEM, distance to river, distance to fire station, and population density. Eventually, the flood risk map was derived from the vulnerability and flood hazard maps to present the risk level of the flood disaster in the residential areas.
Zulfahmi Afifi; Hone-Jay Chu; Yen-Lien Kuo; Yung-Chia Hsu; Hock-Kiet Wong; Muhammad Zeeshan Ali. Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation. Water 2019, 11, 751 .
AMA StyleZulfahmi Afifi, Hone-Jay Chu, Yen-Lien Kuo, Yung-Chia Hsu, Hock-Kiet Wong, Muhammad Zeeshan Ali. Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation. Water. 2019; 11 (4):751.
Chicago/Turabian StyleZulfahmi Afifi; Hone-Jay Chu; Yen-Lien Kuo; Yung-Chia Hsu; Hock-Kiet Wong; Muhammad Zeeshan Ali. 2019. "Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation." Water 11, no. 4: 751.
Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill.
Hone-Jay Chu; Yi-Chin Chen; Muhammad Zeeshan Ali; Bernhard Höfle. Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland. International Journal of Environmental Research and Public Health 2019, 16, 1109 .
AMA StyleHone-Jay Chu, Yi-Chin Chen, Muhammad Zeeshan Ali, Bernhard Höfle. Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland. International Journal of Environmental Research and Public Health. 2019; 16 (7):1109.
Chicago/Turabian StyleHone-Jay Chu; Yi-Chin Chen; Muhammad Zeeshan Ali; Bernhard Höfle. 2019. "Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland." International Journal of Environmental Research and Public Health 16, no. 7: 1109.
Landsliding usually occurs on specific hillslope aspect, which may reflect the control of specific geo‐environmental factors, triggering factors, or their interaction. To explore this notion, this study used island–wide landslide inventories of the Chi–Chi earthquake in 1999 (Mw = 7.6) and Typhoon Morakot in 2009 in Taiwan to investigate the preferential orientation of landslides and the controls of landslide triggers and geological settings. The results showed two patterns. The orientations of earthquake‐triggered landslides were toward the aspect facing away from the epicenter in areas with PGA ≥ 0.6 g and landslide ratio ≥ 1%, suggesting that the orientations were controlled by seismic wave propagation. Rainfall‐triggered landslides tended to occur on dip slopes, instead of the windward slopes, suggesting that geological settings were a more effective control of the mass wasting processes on hillslope scale than the rainfall condition. This study highlights the importance of the endogenic processes, namely seismic wave and geological settings, on the predesigned orientation of landslides triggered by either earthquake or rainfall, which can in turn improve our knowledge of landscape evolution and landslide prediction.
Yi-Chin Chen; Kang-Tsung Chang; Su-Fen Wang; Jr-Chuan Huang; Cheng-Ku Yu; Jien-Yi Tu; Hone-Jay Chu; Cheng-Chien Liu. Controls of preferential orientation of earthquake‐ and rainfall‐triggered landslides in Taiwan's orogenic mountain belt. Earth Surface Processes and Landforms 2019, 1 .
AMA StyleYi-Chin Chen, Kang-Tsung Chang, Su-Fen Wang, Jr-Chuan Huang, Cheng-Ku Yu, Jien-Yi Tu, Hone-Jay Chu, Cheng-Chien Liu. Controls of preferential orientation of earthquake‐ and rainfall‐triggered landslides in Taiwan's orogenic mountain belt. Earth Surface Processes and Landforms. 2019; ():1.
Chicago/Turabian StyleYi-Chin Chen; Kang-Tsung Chang; Su-Fen Wang; Jr-Chuan Huang; Cheng-Ku Yu; Jien-Yi Tu; Hone-Jay Chu; Cheng-Chien Liu. 2019. "Controls of preferential orientation of earthquake‐ and rainfall‐triggered landslides in Taiwan's orogenic mountain belt." Earth Surface Processes and Landforms , no. : 1.
Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery.
Hone-Jay Chu; Chen-Han Yang; Chelsea C. Chou. Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light. ISPRS International Journal of Geo-Information 2019, 8, 26 .
AMA StyleHone-Jay Chu, Chen-Han Yang, Chelsea C. Chou. Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light. ISPRS International Journal of Geo-Information. 2019; 8 (1):26.
Chicago/Turabian StyleHone-Jay Chu; Chen-Han Yang; Chelsea C. Chou. 2019. "Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light." ISPRS International Journal of Geo-Information 8, no. 1: 26.
An uncertainty in the relationship between aerosol optical depth (AOD) and fine particulate matter (PM2.5) comes from the uncertainty of AOD by aerosol models and the estimated surface reflectance, a mismatch in spatiotemporal resolution, integration of AOD and PM2.5 data, and data modeling. In this study, an integrated geographically temporally weighted regression (GTWR) and RANdom SAmple Consensus (RANSAC) models, which provide fine goodness-of-fit between observed PM2.5 and AOD data, were used for mapping of PM2.5 over Taiwan for the year 2014. For this, dark target (DT) AOD observations at 3-km resolution (DT3K) only for high-quality assurance flag (QA = 3) were obtained from the scientific data set (SDS) “Optical_Depth_Land_And_Ocean”. AOD observations were also obtained from the merged DT and DB (deep blue) product (DTB3K) which was generated using the simplified merge scheme (SMS), i.e., using an average of the DT and DB highest quality AOD retrievals or the available one. The GTWR model integrated with RANSAC can use the effective sampling and fitting to overcome the estimation problem of AOD-PM2.5 with the uncertainty and outliers of observation data. Results showed that the model dealing with spatiotemporal heterogeneity and uncertainty is a powerful tool to infer patterns of PM2.5 from a RANSAC subset samples. Moreover, spatial variability and hotspot analysis were applied after PM2.5 mapping. The hotspot and spatial variability of PM2.5 maps can give us a summary of the spatiotemporal patterns of PM2.5 variations.
HoneJay Chu; Muhammad Bilal. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models. Environmental Science and Pollution Research 2018, 26, 1902 -1910.
AMA StyleHoneJay Chu, Muhammad Bilal. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models. Environmental Science and Pollution Research. 2018; 26 (2):1902-1910.
Chicago/Turabian StyleHoneJay Chu; Muhammad Bilal. 2018. "PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models." Environmental Science and Pollution Research 26, no. 2: 1902-1910.
Groundwater drought index characterizes hydrological drought, aquifer characteristics and human disturbance in the hydrological system. For drought management, the values of standardized groundwater index (SGI) at local and regional scales are usually determined in a specific site and regional area. The SGI in the studied area is influenced mainly by precipitation, hydrogeology, and human disturbance occurring in the high-usage pumping area. The underlying signals of SGI at local and regional scales can therefore be identified using data clustering and decomposition analysis e.g. empirical orthogonal functions (EOFs). Using cluster analysis, the three primary SGI clusters of the investigated aquifer are identified to be situated at the proximal fan, mid-fan, and distal fan areas. With EOF, the meteorological drought pattern and the trend of long-term pumping in the aquifer are also identified. Specifically, the meteorological drought pattern is mainly from the proximal fan, while the over-pumping signal is from the coastal area of the distal fan. The regional SGI integrated with EOF is a useful and direct way for detecting and quantifying groundwater drought. The proposed method for identifying drought signals and sustainable zone for water supply is a substantial step toward an effective regional groundwater resource planning.
Hone-Jay Chu. Drought Detection of Regional Nonparametric Standardized Groundwater Index. Water Resources Management 2018, 32, 3119 -3134.
AMA StyleHone-Jay Chu. Drought Detection of Regional Nonparametric Standardized Groundwater Index. Water Resources Management. 2018; 32 (9):3119-3134.
Chicago/Turabian StyleHone-Jay Chu. 2018. "Drought Detection of Regional Nonparametric Standardized Groundwater Index." Water Resources Management 32, no. 9: 3119-3134.