Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN

The increase of urban impervious surface has significant influence on the urban ecological environment, also contributes to urban waterlogging and urban heat island (UHI). Therefore, the remote sensing extraction of urban impervious surface becomes a hotspot. The widely used sub-pixel approach - Linear Spectral Mixture Analysis (LSMA) estimates the impervious surface by adding the high albedo (the bright impervious surface) and the low albedo (the dark impervious surface) from the solving percentage of different objects from mixed pixels. However for the approach, the selection of end members is difficult and some misclassifications will occur, which causes a large area error, and low accuracy of the impervious surface area. For these issue, in this study, a modified LSMA is explored combining with high-resolution image (Google earth image, 2 meters), the Principal Component Analysis (PCA) and the Normalized Difference Building Index (NDBI) to extract the impervious surface of Guangzhou city with Landsat 8 OLI, October 2015. Based on the PCA, and high resolution image are employed to accurate the selection of end members. The LSMA based on the PCA, the modified LSMA based on the PCA and NDBI are taken to extract the impervious surface of the research area. And the Google earth image is for screening the end members and testing the accuracy estimation by sample area. The result shows that after the accurate topographic correction, the modified LSMA based on the PCA and NDBI is better than the conventional LSMA based on the PCA. The area precision of impervious surface in research area by the modified LSMA is improved with the correlation coefficient is 0.947. The study indicates that taking the high resolution images, the PCA and NDBI to improve the LSMA can raise the un-mix accuracy to a certain degree.

Accurate estimations of impervious surface areas are essential for urban planning development. Linear spectral mixture analysis (LSMA) is commonly adopted to extract the impervious surface (IS) fraction in a mixed pixel at the subpixel scale. However, owing to errors in the spectra of pure pixels selected from remote sensing images, incorrect fractions of different land cover types often emerge after unmixing. In this study, two Landsat 8 Operational Land Imager (OLI) images-acquired on 20 September 2019 (Path/Row: 121/44) and 14 November 2019 (Path/Row: 122/44)-of Guangzhou and Shenzhen were unmixed by LSMA using spectral indices in endmember selection. A post-processing model using the Dry Bare-soil Index (DBSI) and Normalized Difference Vegetation Index (NDVI) as thresholds was established to improve the IS fraction of the LSMA result. Comparative analysis reveals that LSMA with the post-processing model achieves better performance for IS fraction extraction (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.910 and 0.926 and root mean square error [RMSE] = 10.08% and 10.83% for Guangzhou and Shenzhen, respectively), and the distribution of IS is basically consistent with the IS of the actual areas. The post-processing model solves the problem of overestimation of pervious surface and underestimation of impervious surface.

This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mixture Analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear Spectral Mixture Analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.

Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks

Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

It is well known that urban impervious surface (IS) has a warming effect on urban land surface temperature (LST). However, the influence of an IS’s structure, components, and spatial distribution on LST has rarely been quantitatively studied within strictly urban areas. Using ETM+ remote sensing images from the downtown area of Shanghai, China in 2010, this study characterized and quantified the influence of the IS spatial pattern on LST by selecting the percent cover of each IS cover feature and ten configuration metrics. The IS fraction was estimated by linear spectral mixture analysis (LSMA), and LST was retrieved using a mono-window algorithm. The results indicate that high fraction IS cover features account for the majority of the study area. The high fraction IS cover features are widely distributed and concentrated in groups, which is similar with that of high temperature zones. Both the percent composition and the configuration of IS cover features greatly affect the magnitude of LST, but the percent composition is a more important factor in determining LST than the configuration of those features. The significances and effects of the given configuration variables on LST vary greatly among IS cover features.

The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unconfined compressive strength (UCS) of fine-grained soil. For that purpose, a database of fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity results of 85 soil specimens has been compiled from the literature. 75 and 10 soil specimens were trained and tested for each model. Six training databases have been prepared to analyze the effect of quality and quantity of training database by selecting 50%, 60%, 70%, 80%, 90%, and 100% of 75 soil specimens. The performance comparison demonstrated that the LSTM model (MD 113) requires fewer training datasets (50% of 75) than the LSSVM (MD 102 and MD 108) and ANN (MD 120, MD 127, MD 136, MD139, MD 148, and MD 150) models. Also, it was observed that the nonlinear LSSVM model (MD 108) is unaffected by multicollinearity in training datasets and predicted UCS better than the linear LSSVM model (MD 102). Furthermore, the Levenberg-Marquardt neural network model (MD 120) has outperformed the other ANN models with the root mean square error (RMSE) of 5.1214 N/cm2, the mean absolute error (MAE) of 4.1379 N/cm2, and correlation (R) of 0.9836. The overall performance comparison revealed that the LSTM model is more potent than the LSSVM and ANN models. The LSTM model predicted the UCS of fine-grained soil with the RMSE of 4.7539 N/cm2, the MAE of 4.2461 N/cm2, and R of 0.9880. Conversely, cosine amplitude sensitivity analysis demonstrated that the fine content and dry unit weight influence the prediction of virgin UCS of fine-grained soils.

Given the potential importance of solar quiet (Sq) ionospheric current in geomagnetic field modeling, it is vital to obtain accurate parameters characterizing its variations, particularly the spatial and temporal variations. In this paper, we derived the Sq current function based on the spherical harmonic analysis (SHA) technique using a 14‐year (2006–2019) quiet geomagnetic field record over the American sector. The empirical orthogonal function (EOF) analysis was then applied to deduce temporal and spatial variations of the Sq current. It is observed that the first EOF mode of the Sq current function is dominated by solar activity, while the second and third EOF modes exhibit annual and semiannual variations, respectively. Also, the artificial neural network (ANN) model of Sq current function was constructed to validate the EOF model predictions. While the Sq current intensity predicted by the ANN model is underestimated by 2.83%, the EOF model underpredicted the Sq current intensity by 1.92% relative to the observation. The root mean square error (RMSE) of the EOF model is 0.64 kA. This RMSE is about 79% smaller than that of the ANN model. In addition, both the EOF and ANN models capture the variation of the total Sq current (Jtotal) intensity with respect to solar activity. In principle, the EOF model had an optimal performance at nearly 98% accuracy, with the ANN model exhibiting almost the same degree of accuracy, which appears to be a reference point for ionospheric conditions when looking for space weather applications.

Based on Landsat ETM+ data within the metropolitan area of Lanzhou, China, green vegetation(GV) and impervious surface was extracted by a constrained linear spectral mixture analysis (LSMA),together with single window algorithm to invert land surface temperature ,and the correlation analysis was then conducted to examine the relationship between urban heat island (UHI) effect and impervious surface. Four types of end members with high albedo, GV, soil and low albedo are selected to model complicated urban land cover, estimation accuracy is assessed using Root-Mean-Square (RMS)error and color aerial images, with the help of Mantel and Partial Mantel. Spatial relationship of land surface temperatures (LST), impervious surface and GV were analyzed. Results indicate that impervious surface distribution and GV can be derived from Landsat TM/ETM+ images with satisfactory precision. Impervious surface and GV were positively correlated with UHI, while LST has space dependence, it has high space dependence, and was higher correlated with impervious surface than GV.

Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modeling and simulation for accurate prediction. An artifi cial intelligence technology allows knowledge processing and can be used as forecasting tool. The application of Artifi cial Neural Network (ANN), to predict the behaviors of nonlinear systems has become an att ractive alternative to traditional statistical method. This study introduces two fundamentally diff erent approaches for designing a model, the statistical method based on Multiple Regression Analysis (MRA) and the emerging computationally powerful techniques based on ANN. To evaluate the prediction effi ciency, twenty (20) years of mean annual rainfall data from year 1990 to 2010 and within the 75 km radius of PAG-ASA, Echague, Isabela, Philippines was used. The ANN and the MRA approaches are applied to the data to derive the weights and the coeffi cients respectively. Bett er performance of the ANN or MRA models were measured by the criteria of the least Root Mean Square Error (RMSE), and highest Effi ciency Coeffi cient (EC). The study reveals that ANN model can be used as an appropriate forecasting tool to predict the rainfall, which out performs the MRA model in the province of Isabela. Keywords - Artifi cial Neural Network, Multiple Regression Analysis

Purpose – This paper aims to predict the needle penetration force (NPF) in denim fabrics using the artificial neural network (ANN) and multiple linear regression (MLR) models based on the effects of various sewing parameters. Design/methodology/approach – In order to design the ANN and MLR models, four parameters including fabric weight, number of fabric layers, weave pattern, and sewing needle size are taken into account as the input parameters and NPF as the output parameter. According to these parameters, 140 samples of data were resulted. Each sample was tested five times. From these 140 data (input-output data pairs), 112 were used for training the ANN and MLR models and 28 samples were used to test the performance of ANN and MLR. Also, the NPF was measured on the Instron tensile tester to simulate sewing process. Findings – The results indicated that the NPF in denim fabrics can be well predicted in terms of sewing parameters by using ANN and MLR models, in which the ANN model exhibits greater performance than MLR (RANN=0.989&gt; RMLR=0.901). Research limitations/implications – The NPF measurement method is limited at low speed. Originality/value – Using the ANN model for forecasting NPF in denim fabrics can help the garment manufactures to produce high-quality denim products and improve the sewing process through reducing seam damage. The NPF could be also measured in the cycle loading conditions similar to sewing machine process by using a special designed tools mounted on the Instron tensile tester.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28 vibration data measured, 20 were used for ANN training, 4 for validation and the remaining 4 for testing. In the model, peak particle velocity (PPV) was used as the output parameter, and the maximum explosive amount per delay and scaled distance were used as input parameters. In addition, MAPE, RMSE and R2 performance criteria were calculated from the realized, predicted by ANN and PPV values obtained from the field equation. The maximum amount of explosives used per delay and the sensitivity analysis of the scaled distance on the highest particle velocity were also determined. As a result, when the vibration data calculated from the field equation and estimated from the ANN model were compared with the realized vibration data, it was seen that the values obtained by the ANN model had a higher correlation.