GIS Platform Research Articles

A universal parallel scheduling approach to polyline and polygon vector data buffer analysis on conventional GIS platforms

AbstractWith the increasing volume of spatial data, conventional vector data buffer analysis algorithms cannot meet the demands of fast data processing, so parallel computing is introduced to accelerate vector data analysis. However, it is difficult for these GIS platforms to adopt parallel approaches to conduct buffer analysis due to their conventional data format and vector buffer analysis algorithms. To address the problem, a universal parallel scheduling approach to buffer analysis on conventional GIS platforms is proposed in this study. First, the key impacting factor of buffer analysis computing time is identified. The relationship between the impacting factor and the buffer analysis computing time is analyzed to generate computational intensity transformation functions. Then, computational intensity grids (CIGs) of polyline and polygon are constructed by partially computing the computational intensity of a lattice. Using the corresponding CIGs, a two‐stage adaptive spatial decomposition method for parallel buffer analysis is developed. Firstly, the whole domain of the spatial vector dataset is divided into sub‐domains, with the computational intensity of each sub‐domain being equal as far as possible; secondly, the features are evenly assigned within the sub‐domains into parallel buffer analysis tasks for load balance. The experiments demonstrate that the approach presented in this article can effectively evaluate and spatially represent the computational intensity of buffer analysis for polylines and polygons. Compared with typical spatial adaptive decomposition methods and regular domain decomposition methods, the new approach accomplishes greater balanced decomposition of computational intensity for parallel buffer analysis and achieves near‐linear speedups. The new approach achieves excellent performance on three conventional GIS platforms, indicating that our approach is an effective parallel scheduling approach to vector data buffer analysis for conventional GIS platforms.

Predicting forest fire risk based on mining rules with ant-miner algorithm in cloud-rich areas

Annually, millions of hectares of forest lands around the world are destroyed by fires. To minimize the fire-caused losses, more studies on the risk prediction of forest fires need to be carried out. For predicting the risk of forest fires in cloud-rich areas (e.g., the southwest of China), the synergetic use of operational forecasting systems and remote sensing-based models is expected to have a consistent performance. Therefore, we proposed in this study a new model based on ant-miner algorithm which has a good capability of solving multivariable and non-linear problems in the synergetic modeling of multi-source data. Based on historical fire data during 2000–2018 in Chongqing city, its performance was tested, and then was compared with that of other three models (i.e., meteorological data-, Artificial Neural Network-, and Support Vector Machine-based models). Results showed that, without interference from human factors, the risk predictions of proposed model were more objective. And, its mined-rules were easier to understand and also portable across multiple GIS platforms. Moreover, the proposed model has a better performance at predicting risk levels (i.e., overall accuracy was 79.02% and Kappa coefficient was 0.678) and the spatial distribution of its predictions were more detailed. This research indicated that the ant-miner algorithm-based model was more effective and reliable, and it could be used for constructing the operational system of risk predictions for forest fires in cloud-rich areas.

ASSESSING RESILIENCE OF INFRASTRUCTURES TOWARDS EXOGENOUS EVENTS BY USING PS-INSAR-BASED SURFACE MOTION ESTIMATES AND MACHINE LEARNING REGRESSION TECHNIQUES

Abstract. Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities.

RESCUE MANAGEMENT AND ASSESSMENT OF STRUCTURAL DAMAGE BY UAV IN POST-SEISMIC EMERGENCY

Abstract. The increasing frequency of emergencies urges the need for a detailed and thorough knowledge of the landscape. The first hours after a disaster are not only chaotic and problematic, but also decisive to successfully save lives and reduce damage to the building stock. One of the most important factors in any emergency response is to get an adequate awareness of the real situation, what is only possible after a thorough analysis of all the available information obtained through the Italian protocol Topography Applied to Rescue. To this purpose geomatic tools are perfectly suited to create, manage and dynamically enrich an organized archive of data to have a quick and functional access to information useful for several types of analysis, helping to develop solutions to manage the emergency and improving the success of rescue operations. Moreover, during an emergency like an earthquake, the conventional inspection to assess the damage status of buildings requires special tools and a lot of time. Therefore, given the large number of buildings requiring safety measures and rehabilitation, efficient use of limited resources such as time and equipment, as well as the safety of the involved personnel are important aspects. The applications shown in the paper are intended to underline how the above-mentioned objective, in particular the rehabilitation interventions of the built heritage, can be achieved through the use of data acquired from UAV platform integrated with geographic data stored in GIS platforms.

3D MAP SYSTEM FOR TREE MONITORING IN HONG KONG USING GOOGLE STREET VIEW IMAGERY AND DEEP LEARNING

Abstract. In densely built urban areas such as Hong Kong, the positive effect of urban trees is to help maintain high environmental and social sustainability for the city while unmanaged trees lead to negative effects such as accidents, outbreaks of pests and diseases. The public awareness of urban tree population has been increasing and preserving all the benefits offered by trees, a continuous monitoring concept would be required. In this work, an efficient 3D map system for tree inventory in Hong Kong is presented to the based on automated tree detection from publicly available Google street view (GSV) panorama images. First, Convolutional Neural Networks (CNNs) based object detector and classifier – YOLOv3 with pretrained model is adopted to learn GSV images to detect tree objects. GSV depth image has been utilized to decode depth values of each GSV panorama image and will provide accurate information to calculate the tree geographic position. A “field of view” filter was designed to remove duplicated tree detection within the overlapped areas followed by spatial clustering applied to further increase the tree localization accuracy. The average distance between the detected trees and ground truth data was achieved within 3 meters for selected roads used for the experiment. Second, a 3D Map platform prototype for facilitating the urban tree monitoring and management was developed. Currently, there is no true 3D platform for interpreting the results of tree records in Hong Kong city areas. With the help of webGL technology, contemporary browsers are able to show 3D buildings, terrain and other scene components together with the obtained tree records in an open source 3D GIS platform, the level of visualization is enhanced as all the detected trees are placed on the 3D digital terrain model. Consequently, it is easy for end-users to know the actual position of the trees and their distribution.

AN INTERDISCIPLINARY APPROACH FOR THE SEISMIC VULNERABILITY ASSESSMENT OF HISTORICAL CENTRES IN MASONRY BUILDING AGGREGATES: APPLICATION TO THE CITY OF SCARPERIA, ITALY

Abstract. The seismic vulnerability of masonry building aggregates is very difficult to determine, since it is affected by many uncertainties. The most uncertain quantities concern the historical periodization of structural aggregates. Moreover, the studies made at the urban scale can hardly be thorough, and usually the knowledge achieved on the single units is not fully satisfactory, so that the structural designer has to deal with uncompleted architectonical surveys and partial data; one of the most important problems concerns the lack of knowledge about the boundary conditions between adjacent structures. In order to perform mechanical analyses, an extensive knowledge of materials and techniques adopted is required. In this paper, an integrated methodology for the seismic assessment of building aggregate is presented. It concerns a multidisciplinary knowledge-based approach calibrated over the historical centres and the urban aggregates; the procedure joins different aspects, such as the use of modern technologies for an integrated knowledge, plans reconstructions through archival documents, laser scanner digital survey of urban fronts, non-destructive investigations of the materials. GIS and BIM platforms have been used to implement and collect data in order to perform detailed analyses. The information allowed to assess the seismic vulnerability of the building aggregates and the expected damage scenarios through empirical methodologies. The city of Scarperia, founded a few kilometres from Florence during the Medieval Age and characterized by a medium seismicity, has been chosen as a case study for the presented procedure.

Detecting latent urban mobility structure using mobile phone data

The spatial heterogeneity of land use patterns and residents’ corresponding economic activities give rise to urban mobility’s latent structure, which is of great importance for urban planning and transport infrastructure investment but cannot be readily captured using conventional data sources. We developed a methodological framework for detecting urban mobility structure at the transportation analysis zone (TAZ) level in Beijing using mobile phone signal data. First, we derived origin–destination data at the TAZ level from mobile phone data and visualized them in ArcGIS. Next, we improved community detecting algorithms generally used in social networks by reversing distance weight, such as by dividing ODs by 1, and used the results to reveal hidden clustering features of TAZs, according ODs between them. We visualized and analyzed population density, OD spatial distribution at different times, and ratio of daytime to nighttime population using the GIS platform; all showed some spatial cluster features. We then applied a structure detection algorithm using ODs between TAZ pairs to identify the hidden structure of urban mobility extracted from phone data. For Beijing, the identified mobility structure contains 27 clusters, with those in suburban areas tending to match administrative boundaries well but those in the developed center areas showing complex distributions and matching administrative boundaries poorly. Authorities that provide mobility infrastructure can use the resulting insights into urban planning and transportation planning to inform policy decisions at the local and city levels.

Delineation of groundwater potential zones in Karha river basin, Maharashtra, India, using AHP and geospatial techniques

Groundwater is a life-sustaining resource catering the daily water requirements of mankind, aids in industrial development, influences agricultural activity, and maintains the ecological balance. The present study was carried out in the Karha river basin, Maharashtra State, having an area of 1314.98 km2. In hydro-geological research, for the exploration of groundwater resources, the integration of remote sensing data and GIS plays a remarkable role in monitoring, assessing, and conserving groundwater resources for water resource management and development. A total ten thematic layers such as geomorphology, geology, land use/land cover, drainage density, slope angle, lineament density, rainfall distribution map, curvature, topographical wetness index, and soil map were integrated into a GIS platform, using the spatial analyst tool in Arc GIS 10.0 to delineate the Ground Water Potential Zones in Karha river basin area. The analytical hierarchy process (AHP) technique is used to determine the weighted value for each parameter and their sub-parameters, based on their relative importance of influencing factors for groundwater recharge. The final groundwater potential zonation map of the study area was categorized into four classes namely, poor, moderate, good, and excellent potential zones. The western part of the basin, particularly in Ghorawadi, Garade, and Saswad regions, have been identified as excellent potential zones for groundwater exploration. The result has shown about 21.96% (285.43 km2) and 28.82% (374.49 km2) of the study area falls under “excellent” and “good” groundwater potential zone, respectively, whereas about 10.81% (140.46 km2) area falls under the poor potential zone.

Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.

The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.

Integrating cellular automata and discrete global grid systems: a case study into wildfire modelling

Abstract. With new forms of digital spatial data driving new applications for monitoring and understanding environmental change, there are growing demands on traditional GIS tools for spatial data storage, management and processing. Discrete Global Grid System (DGGS) are methods to tessellate globe into multiresolution grids, which represent a global spatial fabric capable of storing heterogeneous spatial data, and improved performance in data access, retrieval, and analysis. While DGGS-based GIS may hold potential for next-generation big data GIS platforms, few of studies have tried to implement them as a framework for operational spatial analysis. Cellular Automata (CA) is a classic dynamic modeling framework which has been used with traditional raster data model for various environmental modeling such as wildfire modeling, urban expansion modeling and so on. The main objectives of this paper are to (i) investigate the possibility of using DGGS for running dynamic spatial analysis, (ii) evaluate CA as a generic data model for dynamic phenomena modeling within a DGGS data model and (iii) evaluate an in-database approach for CA modelling. To do so, a case study into wildfire spread modelling is developed. Results demonstrate that using a DGGS data model not only provides the ability to integrate different data sources, but also provides a framework to do spatial analysis without using geometry-based analysis. This results in a simplified architecture and common spatial fabric to support development of a wide array of spatial algorithms. While considerable work remains to be done, CA modelling within a DGGS-based GIS is a robust and flexible modelling framework for big-data GIS analysis in an environmental monitoring context.

Chinese Temple Networks in Southeast Asia: A WebGIS Digital Humanities Platform for the Collaborative Study of the Chinese Diaspora in Southeast Asia

This article introduces a digital platform for collaborative research on the Chinese diaspora in Southeast Asia, focusing on networks of Chinese temples and associations extending from Southeast China to the various port cities of Southeast Asia. The Singapore Historical Geographic Information System (SHGIS) and the Singapore Biographical Database (SBDB) are expandable WebGIS platforms gathering and linking data on cultural and religious networks across Southeast Asia. This inter-connected platform can be expanded to cover not only Singapore but all of Southeast Asia. We have added layers of data that go beyond Chinese Taoist, Buddhist, and popular god temples to also display the distributions of a wide range of other religious networks, including Christian churches, Islamic mosques, Hindu temples, and Theravadin, which are the Taiwanese, Japanese and Tibetan Buddhist monasteries found across the region. This digital platform covers a larger area than the Taiwan History and Culture in Time and Space (THCTS) historical GIS platform but is more regionally focused than the ECAI (Electronic Cultural Atlas Initiative) By incorporating Chinese inscriptions, extensive surveys of Chinese temples and associations, as well as archival and historical sources, this platform provides new materials and new perspectives on the Chinese diaspora in Southeast Asia. This paper: (1) outlines key research questions underlying these digital humanities platforms; (2) describes the overall architecture and the kinds of data included in the SHGIS and the SBDB; (3) reviews past research on historical GIS; and provides (4) a discussion of how incorporating Chinese epigraphy of Southeast Asia into these websites can help scholars trace networks across the entire region, potentially enabling comparative work on a wide range of religious networks in the region. Part 5 of the paper outlines technical aspects of the WebGIS platform.

The long-term evolution of landslide activity near the epicentral area of the 2008 Wenchuan earthquake in China

The Wenchuan earthquake triggered numerous landslides and damaged vast areas of vegetation. Strong mountain earthquakes have long-term impacts on the activities of subsequent landslides. It is crucial for long-term geohazard risk assessment to investigate the spatio-temporal evolution of post-earthquake landslides. We used multi-year images from 2008 to 2018 to track the reactivation of coseismic landslides and newly occurring landslides to evaluate the influence of the Wenchuan earthquake on subsequent rain-induced landslides. With the aid of a GIS platform combined with field investigations, we analyzed the temporal and spatial evolutions of landslide activities over the past ten years and the factors controlling these changes. In addition, the process of vegetation recovery at landslide sites in the epicentral area was detected based on multi-year Landsat TM data after the Wenchuan earthquake. We observed that the total area of active landslides decreased significantly with time. In the first 5 years (2008–2013), the vegetation recovered quickly but was not well rehabilitated, and post-seismic landslides generally maintained high activity levels. Due to progressive revegetation and consolidation, most of the coseismic deposits along the hillslopes gradually stabilized. Subsequently (2013–2018), landslide activity weakened and mainly occurred in high and steep areas with poor vegetation recovery, particularly in proximity to the drainage network. Decreasing occurrence of rain-induced landslides was closely related to vegetation recovery in both time and space, and vegetation recovery could promote the reduction in post-earthquake landslide activities. We also proposed a quantitative model for the landslide rate in the epicentral area of the Wenchuan earthquake, which shows that the enhanced landslide rate reached its peak after the earthquake and will decrease to the pre-earthquake level in 25 years. The evolution of the post-seismic landslide activity can be categorized into four stages: high active period (2008–2013), low active period (2013–2018), recovery period (2018–2033), and stable period (after 2033).

Assessment of Relative Tectonics Activity Zones in Masila Oil Field, Yemen

Masila oil field, a part of rift graben basin (Sayun-Masila basin), Yemen, is affected by several tectonic events since Pre-Cambrian period. The present study makes an effort to assess the relative tectonic activity in this oil field. Several geomorphic indices, lineaments, and depth structural anomalies were generated as thematic maps using remotely sensed data, wells data, and existing geological and seismic data in a GIS platform. The thematic maps were converted into a grid format, classified into different categories based on their relative importance, and integrated using the GIS integration tool. The final map, generated by the integration of thematic maps, was categorized into five relative tectonic zones, namely very high relative tectonic zone (9.2%), high relative tectonic zone (10.6%), moderate relative tectonic zone (30%), low relative tectonic zone (36%), and very low relative tectonic zone (14.2%). The surface lineament anomalies and subsurface geological cross-section were subsequently used to validate the derived results. The results obtained through this approach may be helpful for exploring resources, such as hydrocarbons, and predicting hazards. The study highlights the advantage of using different datasets from various sources and their overlay in a GIS platform for assessing relative tectonic activity.

Development of maximum depth-area-duration curve for Udaipur district using Remote Sensing and GIS platform

Development of maximum depth-area-duration curve for Udaipur district using Remote Sensing and GIS platform

Multi-view crowd congestion monitoring system based on an ensemble of convolutional neural network classifiers

Multi-view video surveillance is a highly valuable tool to ensure the safety of the crowd in large public space. By utilizing complementary information captured by multiple cameras, the issue of limited views and occlusion in single views can be addressed to gain better insight into the whole monitored space. However, multi-view surveillance has been widely applied to microscopic crowd analysis, for example pedestrian detection and tracking, while macroscopic level analysis, which deals with the whole crowd, has received little attention. We propose a multi-view framework for the generation of level of service maps, which are the most commonly used measure of congestion at macroscopic level, based on an ensemble of state-of-the-art Convolutional Neural Networks (CNNs). Several combination rules are compared and evaluated on two datasets, both in sparse and dense scenarios. Our results show that this fusion framework improves the accuracy of level of service map generation, from 83.2% to 89.8%, and eliminates blind spots in single views. Our framework is implemented on a 3 D GIS platform, which provides a suitable interface for multi-view crowd congestion management. The results of a loading test show that a maximum of 48 cameras can be processed at a map refresh rate of 2 seconds.

Evaluation of Geological and Ecological Bearing Capacity and Spatial Pattern along Du-Wen Road Based on the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) Method

As China is a mountainous country, a large quantity of the population has to live in mountainous areas due to limited living space. Most of them cluster along roads in areas with relatively poor traffic conditions. In view of the spatial-temporal change of complex geological and ecological environment along the roads in the mountains, this paper takes the Dujiangyan- Wenchuan (Du-Wen) Road as the research object, and puts forward a method to evaluate the bearing capacity of regional geological and ecological environment based on the evaluation of quality and spatial coupling of bearing capacity. For the needs of the current research, a total number of 20 indicators from three aspects of geological, ecological, and social attributes were selected to carry out the assessment. Based on the GIS platform and evaluation index system, the weight of each evaluation index factor is determined by Analytic Hierarchy Process (AHP). The comprehensive quality of bearing capacity is calculated by the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) algorithm through weighted superposition, which comes to result in the evaluation of geological, ecological, and social environment. Afterward, the bearing capacity of the study area is classified, combining the results of hot spot analysis. The study shows that the spatial distribution of geological, ecological, and socio-economic bearing capacity in this area is highly aggregated, with 31.12% of the area to be classified as suitable construction area, 31.98% as backup reserve area, and 36.79% as unsuitable construction area. The studied triangle area, composed of Yingxiu Town, Xuankou Town, and Dujiangyan City, presents a large area of a high-valued aggregation area, with comprehensive high quality bearing capacity and spatial aggregation, which is better for planning and construction.

Change Pattern and Driving Mechanism of Construction Land in China’s Undertaking Industrial Transfer Demonstration Area: Taking the Wanjiang City Belt along the Yangtze River as an Example

The construction land includes the urban land, rural residential areas and other construction land. The Wanjiang City Belt along the Yangtze River is an important demonstration area for undertaking industrial transfer in China. With the accumulation of factors relative to economic development, the construction land has increased sharply, and the regional ecological security pattern is facing new challenges. After collecting the image interpretation data of multi-period land use of the Wanjiang City Belt, the work studied the characteristics of construction land change pattern since 1995 and its driving mechanism based on the GIS platform, land use transfer matrix, expansion intensity index, hotspot analysis and mathematical statistics. The results showed that: (1) From 1995 to 2015, the urban land and other construction land in the Wanjiang City Belt have increased, but the rural residential areas decreased in 2010-2015. The three types of land had the largest changes in 2005-2010 and the change in the other construction land was particularly prominent. (2) The hotspots for construction land expansion are mainly in urban areas with rapid economic development such as Hefei, Wuhu, Ma’anshan and Tongling, where the land use changes most severely. (3) The driving factors for the change of construction land area include natural and social factors. Among social and economic factors, the GDP, industrial added value, secondary output value and urbanization rate are the main driving forces for changes. In the past 20 years, the construction of China’s Undertaking Industrial Transfer Demonstration Area has changed the land optimal allocation and intensive use mode in the region, providing the basis for resource development and utilization, economic development and industrial structure adjustment.

Determination of protection zones in drinking water basins: a case study from Turkey, Sapanca Lake Basin

To prepare specific protection and policies for drinking water basins is important for providing sustainable drinking water management. In this study, a decision support system composed of hydrogeological, hydrogeochemical, soil type and topological parameters was applied to define the protection zones. Littoral zone and slope parameters in topology, the grain size in soil media, hydraulic conductivity, aquifer type and well parameters in hydrogeology, hydrogeochemical and isotopic assessment were used to create protection zones in the decision support system based on the Geographic Information System. Identifying hydrological resource areas, and determination of residence time and recharge altitude in groundwater were done using hydrogeochemical parameters and isotopic tracers. The GIS platform was used to evaluate the maps generated from the parameters and to apply reclassification and overlapping methods. The proposed methodology was applied to the Sapanca Lake Basin in Turkey since the lake is one of the most crucial drinking water lakes in Turkey. According to the proposed methodology, green belt, wellhead, absolute, short-range groundwater protection, short and medium-range protection zones were defined in the basin. The absolute protection zones in the basin are distinctively the areas covered with karst structures which are dolines and swallow holes.

A Web-Enabled GIS Platform for Agricultural Land Use

One of the important and leading sources of livelihood in Catanduanes is agriculture. Agriculture has been an avenue to help improve the Philippine economy. To further boost the agriculture industry, the researcher aimed to design and develop a web-enabled GIS platform for agricultural land use that contains a database socio-demographic profile and geographic physical information details of agricultural lands of the province. Data collected are subjected to analysis of an approach in the development and implementation of the study and set as bases for government implementation and a possible policy that assist farmers and local communities. The features of the system help the local leaders to strategize activities and easily facilitate the identification, monitoring, forecasting, visualization, and analysis of situations on Agricultural land use. The acceptability rating of the system developed is 4.72 highly acceptable according to system users. The web-enabled GIS serves as an initiative to improve agricultural land use system by integrating GIS in terms of locating, mapping, profiling, and land management in the province. It is expected through this endeavour that an increase in harvest rate will benefit farmers and local communities. Utilization is expected to help combat climate change and preserve the environment and its ecosystem.

Damming effect on habitat quality of riparian corridor

• This work delimits multi-criteria based riparian corridor. • The riparian habitat quality was evaluated for pre and post HA phases. • The riparian zone was decreased by 12.29% in post HA phase. • The very poor riparian quality area has increased over time. • The riparian quality models were validated using ROC, wilcoxon signed rank test. Riparian corridor, a vital ecotonian ecosystem is under stress due to flow reduction and inconsistency of flow in Lower Tangon river basin across the neo tectonically active Barind tract of India and Bangladesh. But it is ill defined and its habitat quality is rarely assessed insipite of its immense socio-ecological importance. The present work intends to delineate the riparian corridor and quality assessment of the riparian habitat in reference to the hydrological alteration (HA) triggered by anthropogenic activities, mainly damming in 1989. The present work applied multi-parametric (altitude, topographic roughness, water body, forest patches) approach for delineating the riparian corridor instead of existing uni-criterion approaches for the same. Quality assessment of the habitat is done using nine surface, ecological, hydrological and anthropogenic parameters in the Arc Gis platform employing advance machine learning algorithms like Fuzzy logic (FL), Artificial Neural Network (ANN), Random Forest (RF), Random subspace, Dagging and Support vector machine (SVM). Total 20.63 km 2 and 18.36 km 2 areas respectively in pre and post-HA periods alongside the main river are identified as the riparian corridor. HA has reduced the area under the riparian corridor by 12.29% in post-HA state. Simultaneously, the quality of the riparian habitat is also deteriorated in the post-HA period. In pre-HA state, 49.05% area was under good to very good riparian habitat but it is reduced to 11.86% in post-HA state. Regularization of the flood, release of ecological flow to the downstream, renovation of tie channels between flood plain and river, restriction in agricultural invasion towards riparian forest and wetland are some of the essential steps for minimizing qualitative deterioration of riparian habitat and amelioration of the ecosystem concerned.