Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models

Landslide susceptibility is a contemporary method for delineation of landslide hazard zones and holistically mitigating the future landslides risks for planning and decision-making. The significance of this study is that it would be the first instance when the ‘geon’ model will be attempted to delineate landslide susceptibility map (LSM) for the complex lesser Himalayan topography as a contemporary LSM technique. This study adopted the per-pixel-based ensemble approaches through modified frequency ratio (MFR) and fuzzy analytical hierarchy process (FAHP) and compared it with the ‘geons’ (object-based) aggregation method to produce an LSM for the lesser Himalayan Kalsi-Chakrata road corridor. For the landslide susceptibility models, 14 landslide conditioning factors were carefully chosen; namely, slope, slope aspect, elevation, lithology, rainfall, seismicity, normalized differential vegetation index, stream power index, land use/land cover, soil, topographical wetness index, and proximity to drainage, road, and fault. The inventory data for the past landslides were derived from preceding satellite images, intensive field surveys, and validation surveys. These inventory data were divided into training and test datasets following the commonly accepted 70:30 ratio. The GIS-based statistical techniques were adopted to establish the correlation between landslide training sites and conditioning factors. To determine the accuracy of the model output, the LSMs accuracy was validated through statistical methods of receiver operating characteristics (ROC) and relative landslide density index (R-index). The accuracy results indicate that the object-based geon methods produced higher accuracy (geon FAHP: 0.934; geon MFR: 0.910) over the per-pixel approaches (FAHP: 0.887; MFR: 0.841). The results noticeably showed that the geon method constructs significant regional units for future mitigation strategies and development. The present study may significantly benefit the decision-makers and regional planners in selecting the appropriate risk mitigation procedures at a local scale to counter the potential damages and losses from landslides in the area

Rockfall Hazard Rating System along SH-72: a case study of Poladpur–Mahabaleshwar road (Western India), Maharashtra, India

Construction of railways, highways and roads around steep slopes is a challenging assignment and require special investigations by geologists and geotechnical engineers that help to identify critical slopes. Rockfall Hazard Rating System for India (RHRSI) is a modified scheme for Indian subcontinent and used to define overall stability of slopes in mountainous region or rock cut slopes. Once this rating system identifies critical zones, proper protection measures can be applied to prevent rockfalls. In this article, RHRSI is adopted to identify slopes, prone to rockfalls so that proper preventive measure can be proposed to mitigate loss. State Highway-72 (SH-72) connecting Poladpur to Mahabaleshwar, is an important transportation corridor supporting high vehicle traffic within the well-known tourist area is chosen for rockfall hazard rating. Field observations show that up to 15 km corridor from Mahabaleshwar town has frequent rockfall problems with two most rockfall locations situated at about 12 and 6 km from Mahabaleshwar town. Also, an attempt has been made to focus on risk-consequence analysis for rockfall locations. To this aim, the RHRSI is applied to SH-72 corridor on two locations which are identified as very prone to rockfall hazard

La dimensión geológica del patrimonio cultural: el caso de las cuevas de Ajanta (Maharashtra, India)

International development for the both geoconservation and geotourism requires attention to all kinds of (potential) geological heritage. The Ajanta Caves (western Maharashtra, India) is a famous cultural object consisting of 30 caves carved in the Deccan Traps and inscribed to the UNESCO list of the World Heritage Sites. Its examination permits to indicate four geological features, which are the artificial caves themselves (these mark geological activity of the man in the historical past), the end-Cretaceous floot basalts (these demonstrate the emplacement of Large Igneous Province and the relevant palaeoenvironmental catastrophe), the gorge of the Waghora River  (this  is peculiar landform resulted from the river erosion of hard rocks), and the rockfall hazard (this is an interesting engineering geological phenomenon linked to the caves construction/maintenance). Geological heritage value of these features is argued. Unfortunately, there is not any geotourism activity at the Ajanta Caves presently. The content analysis of the principal on-line resources (web pages) devoted to this cultural site reveals the absence of sufficient geological information that would facilitate geotourism. Generally, judgements about the Ajanta Caves and the other similar sites in the geological dimension permit to consider the wide spectrum of the geological heritage. They also highlight some extra opportunities for geotourism, which can benefit by its development at cultural sites with thousands of visitors.El desarrollo internacional tanto para la geoconservación como para el geoturismo requiere que se preste atención a todo tipo de patrimonio geológico (potencial). Las cuevas de Ajanta (Maharashtra occidental, India) son un famoso bien cultural que consiste en 30 cuevas talladas en las Traps de Decán e inscritas en la lista de la UNESCO de los sitios del Patrimonio Mundial. Su examen permite indicar cuatro características geológicas, que son las cuevas artificiales propiamente dichas (que marcan la actividad geológica del hombre en el pasado histórico), los basaltos de flujo del final del Cretácico (que demuestran el emplazamiento de la Gran Provincia Ígnea y la pertinente catástrofe paleoambiental), el desfiladero del río Waghora (se trata de una peculiar forma del terreno resultante de la erosión fluvial de las rocas duras), y el peligro de desprendimiento de rocas (se trata de un interesante fenómeno geológico de ingeniería vinculado a la construcción/mantenimiento de las cuevas). Se argumenta el valor de patrimonio geológico de estas características. Lamentablemente, no hay ninguna actividad de geoturismo en las Cuevas de Ajanta en la actualidad. El análisis del contenido de los principales recursos en línea (páginas web) dedicados a este sitio cultural revela la ausencia de suficiente información geológica que facilite el geoturismo. En general, los juicios sobre las Cuevas de Ajanta y los demás sitios similares en la dimensión geológica permiten considerar el amplio espectro del patrimonio geológico. También ponen de relieve algunas oportunidades adicionales para el geoturismo, que puede beneficiarse de su desarrollo en sitios culturales con miles de visitantes

Intelligent technique for prediction of blast fragmentation due to the blasting in tropically weathered limestone

In the rock blasting scenario, the success of fragmentation plays a major role. Prediction of blasted rock mass fragmentation has a significant role in the overall economics of opencast mines. Blast fragmentation has a direct impact on efficiency and cost of operation consisting of loading, transport and crushing. Tropical weathering has a direct impact on rock mass properties and strength of rock. Thus, challenging issues are created for blastability of tropically weathered limestone. It is necessary to analyze the blast fragmentation and optimize the blasting parameters. Selected limestone mine for this study is in Thailand. Various rock mass properties such as GSI, RMR, and stemming length were studied to find out the correlation with rock fragmentation. Stiffness ratio, hole diameter to burden ratio, powder factor, maximum charge per delay, RQD, blastability index (BI), weathering index (WI) and mean block size were input parameters to analyse mean rock fragment size. Total 105 data sets were collected. In this paper, a hybrid model with Artificial neural network (ANN), Particle swarm optimization (PSO) known as PSO-ANN was implemented to analyse the blast fragmentation. Multivariate regression Analysis (MVRA) was also performed. 83 datasets were trained and balance data sets were utilised for testing data. R2 values for training with PSO-ANN and MVRA showed 0.818 and 0.657 respectively. R2 values for testing with PSO-ANN and MVRA showed 0.70 and 0.694 respectively. Thus the hybrid model PSO-ANN was found useful to predict fragmentation

Recent developments in machine learning and flyrock prediction

The blasting techniques are employed in mining and underground works to loosen the rock mass and ease the excavation. The blasting practices are economical and swifter in terms of their engineering application, however, they are of major environmental and safety concerns. The major issues related to blasting are flyrock, air over pressure, and ground vibrations etc. The rock fragments of rockmass are thrown outward after blasting, which can be threat to workers and machineries involved in the work, and sometimes nearby human settlements can be its victim. Therefore, an accurate prediction of the flyrock distance is the needed by mining practitioners. Earlier, experts have developed several empirical methods based on certain known parameters to assess flyrock distance. However, with time they become irrelevant and were easily replaced with advanced machine learning algorithm. The present study reviews some of these latest publications (2019–2021) examining flyrocks through artificial intelligent technique. The study incorporates types of machine learning models employed, input parameters used and number of datasets supporting the models. The input parameters were further classified according to rock-mass properties, blast design at site, and explosives responsible for blasting. Moreover, to compare the reliability of the model coefficient of correlation of the testing data of the all the documented model were evaluated. Rock density, rock mass rating and Shmidt hammer rebound number (SHRN) were found to be uncertain parameters. Artificial Neural Network (ANN) and other hybrid models for prediction of flyrock were compared

River sand mining Vis a Vis manufactured sand for sustainability

Sand and gravels are basic construction raw material globally. With increased economic activity, rate of extraction of sand and gravels has gone up three folds during last two decades reaching 40 billion tonnes per year. Critical hot spots for sand extraction are China and India. Uncontrolled illegal sand mining is common – reduction in ground water level and water transparency, increased water turbidity, adverse impact on birdlife. It is estimated that every year 10 million cubic meters of sand is extracted in Morocco by sand mafias converting large sand beaches into rocky landscape. Singapore has extended its area by 20% by importing 517 million tonnes of sand over last 20 years from neighbouring countries. Some of the countries like India and Malaysia have developed river sand mining guidelines to ensure equilibrium of river, avoid aggradation of hydraulic structures, protection of rivers against erosion, and avoid water pollution. Principal of sustainability is based on equilibrium of meeting business objectives, environmental compliance and social compliance of various stake holders. Tipple bottom line pillars for sustainability include financial stability, environmental stewardship and social equity. For minimizing environmental effect due river sand mining, many countries have adopted technology of manufacturing sand through series of crushers – primary jaw crusher, secondary cone crusher and tertiary vertical shaft impact (VSI) crusher. Japan has developed V7 dry sand manufacturing technology. Manufacturing Sand (M-Sand) has comparable gradation with river sand. Various technical specifications of aggregates and M-Sand adopted by India and US are provided in this paper. There is need of creating R&D facilities in every region for sustainability to develop specifications of M-Sand based on available resources and meet local sand requirement