Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia

Pradhan, Biswajeet,Lee, Saro Springer-Verlag 2010 LANDSLIDES Vol.7 No.1

Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

Pradhan, Biswajeet,Lee, Saro Springer-Verlag 2010 Environmental Earth Sciences Vol.60 No.5

Seasonal Water Change Assessment at Mahanadi River, India using Multi-temporal Data in Google Earth Engine

Jena, Ratiranjan,Pradhan, Biswajeet,Jung, Hyung-Sup,Rai, Abhishek Kumar,Rizeei, Hossein Mojaddadi The Korean Society of Remote Sensing 2020 大韓遠隔探査學會誌 Vol.36 No.1

Seasonal changes in river water vary seasonally as well as locationally, and the assessment is essential. In this study, we used the recent technique of post-classification by using the Google earth engine (GEE) to map the seasonal changes in Mahanadi river of Odisha. However,some fixed problems results during the rainy season that affects the livelihood system of Cuttack such as flooding, drowning of children and waste material deposit. Therefore, this study conducted 1) to map and analyse the water density changes and 2) to analyse the seasonal variation of river water to resolve and prevent problem shortcomings. Our results showed that nine types of variation can be found in the Mahanadi River each year. The increase and decrease of intensity of surface water analysed, and it varies in between -130 to 70 ㎥/nf. The highest frequency change is 2900 Hz near Cuttack city. The pi diagram provides the percentage of seasonal variation that can be observed as permanent water (30%), new seasonal (28%), ephemeral (12%), permanent to seasonal (7%) and seasonal (10%). The analysis is helpful and effective to assess the seasonal variation that can provide a platform for the development of Cuttack city that lies in Mahanadi delta.

Extraction of road features from UAV images using a novel level set segmentation approach

Abolfazl Abdollahi,Biswajeet Pradhan,Nagesh Shukla 서울시립대학교 도시과학연구원 2019 도시과학국제저널 Vol.23 No.3

A novel hybrid technique for road extraction from UAV imagery is presented in this paper. The suggested analysis begins with image segmentation via Trainable Weka Segmentation. This step uses an immense range of image features, such as detectors for edge detection, filters for texture, filters for noise depletion and a membrane finder. Then, a level set method is performed on the segmented images to extract road features. Next, morphological operators are applied on the images for improving extraction precision. Eventually, the road extraction precision is calculated on the basis of manually digitized road layers. Obtained results indicated that the average proportions of completeness, correctness and quality were 93.52%, 85.79% and 81.01%, respectively. Therefore, experimental results validated the superior performance of the proposed hybrid approach in road extraction from UAV images

Seasonal Water Change Assessment at Mahanadi River, India Using Multi-temporal Data in Google Earth Engine

Ratiranjan Jena,Biswajeet Pradhan,정형섭,Abhishek Kumar Rai,Hossein Mojaddadi Rizeei 대한원격탐사학회 2020 大韓遠隔探査學會誌 Vol.36 No.1

Seasonal changes in river water vary seasonally as well as locationally, and the assessment is essential. In this study, we used the recent technique of post-classification by using the Google earth engine (GEE) to map the seasonal changes in Mahanadi river of Odisha. However, some fixed problems results during the rainy season that affects the livelihood system of Cuttack such as flooding, drowning of children and waste material deposit. Therefore, this study conducted 1) to map and analyse the water density changes and 2) to analyse the seasonal variation of river water to resolve and prevent problem shortcomings. Our results showed that nine types of variation can be found in the Mahanadi River each year. The increase and decrease of intensity of surface water analysed, and it varies in between -130 to 70 m3/nf. The highest frequency change is 2900 Hz near Cuttack city. The pi diagram provides the percentage of seasonal variation that can be observed as permanent water (30%), new seasonal (28%), ephemeral (12%), permanent to seasonal (7%) and seasonal (10%). The analysis is helpful and effective to assess the seasonal variation that can provide a platform for the development of Cuttack city that lies in Mahanadi delta.

Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models

Alireza Arabameri,Biswajeet Pradhan,Khalil Rezaei 한국지질과학협의회 2019 Geosciences Journal Vol.23 No.4

Remote sensing is recognized as a powerful and efficient tool that provides a comprehensive view of large areas that are difficult to access, and also reduces costs and shortens the timing of projects. The purpose of this study is to introduce effective parameters using remote sensing data and subsequently predict gully erosion using statistical models of Density Area (DA) and Information Value (IV), and data mining based Random Forest (RF) model and their ensemble. The aforementioned models were employed at the Tororud-Najarabad watershed in the northeastern part of Semnan province, Iran. For this purpose, at first using various resources, the map of the distribution of the gullies was prepared with the help of field visits and Google Earth images. In order to analyse the earth's surface and extraction of topographic parameters, a digital elevation model derived from PALSAR (Phased Array type L-band Synthetic Aperture Radar) radar data with a resolution of 12.5 meters was used. Using literature review, expert opinion and multi-collinearity test, 15 environmental parameters were selected with a resolution of 12.5 meters for the modelling. Results of RF model indicate that parameters of NDVI (normalized difference vegetation index), elevation and land use respectively had the highest effect on the gully erosion. Several techniques such as area under curve (AUC), seed cell area index (SCAI), and Kappa coefficient were used for validation. Results of validation indicated that the combination of bivariate (IV and DA models) with the RF data-mining model has increased their performance. The prediction accuracy of AUC and Kappa values in DA, IV and RF are (0.745, 0.782, and 0.792) and (0.804, 0.852, and 0.860) and these values in ensemble models of DA-RF and IV-RF are (0.845, and 0.911) and (0.872, and 0.951) respectively. Results of SCAI show that ensemble models had a good performance, so that, with increasing of sensitivity, the values of SCAI have decreased. Based on results, determination of gullies and assessing the process of gullying through remote sensing technology in combination with field observations and accurate statistical and computer methods can be a suitable methodology for predicting areas with gully erosion potential.

Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model

Omer Saud Azeez,Biswajeet Pradhan,Ratiranjan Jena,정형섭,Ahmed Abdulkareem Ahmed 대한원격탐사학회 2019 大韓遠隔探査學會誌 Vol.35 No.1

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara–Selatan Expressway–NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decisionmaking tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

Rockfall Source Identification Using a Hybrid Gaussian Mixture-Ensemble Machine Learning Model and LiDAR Data

Ali Mutar Fanos,Biswajeet Pradhan,Shattri Mansor,Zainuddin Md Yusoff,Ahmad Fikri bin Abdullah,정형섭 대한원격탐사학회 2019 大韓遠隔探査學會誌 Vol.35 No.1

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms (ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model

Azeez, Omer Saud,Pradhan, Biswajeet,Jena, Ratiranjan,Jung, Hyung-Sup,Ahmed, Ahmed Abdulkareem The Korean Society of Remote Sensing 2019 大韓遠隔探査學會誌 Vol.35 No.1

Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara-Selatan Expressway-NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.

Land Use Feature Extraction and Sprawl Development Prediction from Quickbird Satellite Imagery Using Dempster-Shafer and Land Transformation Model

Saharkhiz, Maryam Adel,Pradhan, Biswajeet,Rizeei, Hossein Mojaddadi,Jung, Hyung-Sup The Korean Society of Remote Sensing 2020 大韓遠隔探査學會誌 Vol.36 No.1

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.