Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model

Lu, Zhenyu; Im, Jungho; Quackenbush, Lindi J.; Yoo, Sanglim

doi:10.14358/pers.79.9.809

Cited by 13 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RF model exhibits the poorest performance in comparison to other machine learning methods, like the comparison based on in situ SR data. The reason behind comparatively below average performance of RF could be due to the small training dataset [4,63,64]. On the other hand, SVR showed good performance and good agreement with the previous studies, in which SVR worked well with a small sample size of input variables [43].…”

Section: Evaluation Of Machine Learning Regression Using Satellite-desupporting

confidence: 70%

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

et al. 2019

View full text Add to dashboard Cite

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

show abstract

Section: Evaluation Of Machine Learning Regression Using Satellite-desupporting

confidence: 70%

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Unlike other rule-based regression trees, Cubist produces rules, each of which has an associated multivariate regression; therefore, when multiple rules are applied, the final output can be averaged. Cubist has been widely used for regression applications in remote sensing (Brown et al 2008;Im et al 2012;Lu et al 2013;Kim et al 2014;Li et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

et al. 2016

Self Cite

View full text Add to dashboard Cite

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches-random forest, boosted regression trees, and Cubist-were examined for the downscaling of AMSR-E soil moisture (25 9 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through crossvalidation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

show abstract

“…This way, RF overcomes the well-known limitation of CART, in that results are sensitive to the configuration and quality of training data (Lawrence and Wright, 2001;Rhee et al, 2014;Guo and Du, 2017). Thus, RF has recently gained popularity in remote sensing classification and regression Li et al, 2014;Liu et al, 2015;Lu et al, 2013;Park et al, 2016;Yoo et al, 2012). Two approaches are generally adopted to reach a final conclusion from the independent decision trees including a simple majority voting and weighted majority voting strategy for classification, while a final value is either simply averaged from the results of the multiple regression trees or averaged with weights for regression.…”

Section: Deterministic and Probabilistic Approaches For CI Detectionmentioning

confidence: 99%

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Lee

Han

et al. 2017

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm T b was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6-11.2 µm)-(11.2-12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20-40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

show abstract

Remote Sensing-based House Value Estimation Using an Optimized Regional Regression Model

Cited by 13 publications

References 28 publications

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches

Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Contact Info

Product

Resources

About