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Assessing the impact of urbanization on flood patterns in Varanasi, India using Google Earth Engine

Discover Cities 2025年第1期2卷 1-11页

作者： Yadav, Vikas Kainthola, Ashutosh Kushwaha, Gaurav Pandey, Vishnu H. R. Krishna, Abhi S. Geoengineering and Computing Laboratory Department of Geology Banaras Hindu University Varanasi India

Vagaries in weather patterns and rapid urbanization have made flood mapping and monitoring essential for city planners and administrators. The city of Varanasi (India), on the banks of Ganga river, is one of oldest continually inhabited locales in the world and has become the most visited place in India over the past decade. The city has experienced numerous extreme floods (1978, 2013), disrupting lives of the residents and tourists alike. Present study employed the Google Earth Engine (GEE) for flood mapping and monitoring in the region from 2017 to 2023. Using high-resolution satellite imagery processed through GEE, this study mapped flood-prone areas across the urbanization. Changes in normalized difference vegetation index (NDVI) were ascertained for the study period. Rainfall and water table measurements are also taken into account to delve deeper into flood patterns. The relationship between flood extent and rainfall has been explored. Key aspects such as difference layers, affected areas, and exposed populations were examined. The results of the study allude to the risk posed by construction in flood prone zones which puts human lives at severe risk. This city collectively experienced that around 7578 ha of the area and 819,472 people were exposed during the period of the study. The results of the present study can be used by policy makers to better execute and plan infrastructure in the cites and protect human lives.

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Evolution and trend of landslide research in India based on a decade long publication record

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Discover Geoscience 2025年第1期3卷 1-22页

作者： Kainthola, Ashutosh Pandey, Vishnu Himanshu Ratnam Kushwaha, Gaurav Priya, Kumari Singh, P. K. Singh, T. N. Geoengineering and Computing Laboratory Department of Geology Banaras Hindu University Varanasi India Earth and Planetary Science Allahabad University Prayagraj India Department of Civil & Environmental Engineering IIT Patna Patna India

This study analyses trends in landslide publication in India, between 2010 and 2020, focusing on 79 studies, sourced from platforms like Google Scholar, ResearchGate, Web of Science, and ScienceDirect. The analysis reveals that approximately 65% of the publications were featured in Q1 and Q2 journals, with significant contributions from premium institutions like IIT-B (Mumbai), IIT-ISM (Dhanbad), and WIGH (Dehradun). The studies primarily focus on regions such as Uttarakhand, Himachal Pradesh, Tamil Nadu, and Kerala. Remote sensing and GIS emerged as the most frequently used approaches, appearing in 31 studies, followed by empirical methods (25 studies) and numerical techniques (20 studies). Researchers duly examined susceptibility, vulnerability, and risk using statistical techniques, with kinematic analysis and Slope Mass Rating (SMR) being notable empirical methods. The finite-element method was preferred for numerical slope failure analysis. The study found that 57% of the publications relied on existing data without conducting laboratory tests, while the remaining 43% conducted tests focusing on rock strength, shear strength, and physico-mechanical parameters. Direct shear tests and unconfined compressive strength (UCS) were the most commonly performed laboratory tests. Lithological analysis identified 20 different rock/soil types, with gneiss, quartzite, sandstone, and others frequently documented. Mitigation strategies such as informed decision-making, mechanical remediation, drainage system installation, and slope re-scaling were commonly suggested. Among the 31 computer-based programs used, ArcGIS, DIPS, and RS2 were the top tools for assessing mass-movement issues. The most common failure types reported were rock falls, debris flows, and rock slides, with rainfall identified as the primary landslide-triggering factor in 52 instances. The study highlights geology, geomorphology, and hydrology as key aggravating factors for landslide instances.

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Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2023-2027页

作者： Danial Jahed Armaghani Ahmed SalihMohammed Ramesh Murlidhar Bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

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Role of feature importance in geomechanical classification of rock slopes

Deep Resources Engineering

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Deep Resources Engineering 2025年第1期2卷

作者： Ashutosh Kainthola Vishnu Himanshu Ratnam Pandey Gaurav Kushwaha Vikas Yadav Geoengineering and Computing Laboratory Department of Geology Banaras Hindu University Varanasi 221005 India

Rock slope along motorways in the Higher Himalayan terrains are prone to various types of failure. In order to effectively mitigate these failures, a thorough assessment of rock mass behavior is entailed. The present research employs and compares widely practiced geo-mechanical classification schemes viz., RQD, RMR, SMR, Q-slope, and GSI. A 23 km road cut section, along Sangla to Chitkul route, in Higher Himalayan region (India) has been taken up for this work. Total of 18 locations were selected, and their slope and rockmass properties were examined. Afterwards, the most influencing parameters in RMR, SMR, and Q-Slope were evaluated through a machine learning algorithm, i.e., Random Forest. For RMR basic, about 83 % of rock-slopes were designated in good condition and rest were of Fair quality. Evaluation of slope mass rating along all 18-locations highlighted eight-sites as partially unstable, six-sites as partially stable. Remaining four locations varied between, Very Bad to Bad slope-conditions, necessitating the installation of mechanical supports and redesign of slopes. For SMR classification, feature importance analysis revealed the predominance of F3 variable, RQD and intact rock strength. Q-Slope approach was incorporated to identify the most stable steepest angle of the examined locations. For Q-Slope rating, J n and RQD were found to have the most influence in classification of the slopes. Three zones on the basis of GSI-scores have been identified in the study area, i.e., A (65−95), B (45−55), and C (25−35). This study highlights the application of multiple geomechanical classification schemes, demonstrating how each approach can complement the others.

关键词： SMR Q-slope GSI Feature importance Partial dependence Economic Mitigations

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Ensemble Algorithms for Prediction of Ripping Production in Sedimentary Rocks

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Journal of The Institution of Engineers (India): Series D 2025年

作者： Mohamad, Edy Tonnizam Bhatawdekar, Ramesh Murlidhar Pandey, Vishnu Himanshu Ratnam Kainthola, Ashuthosh School of Civil Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Geoengineering and Computing Laboratory Department of Geology Banaras Hindu University Varanasi221005 India

In the earlier stages of mine planning, geo-engineers and planners need a reliable estimation of ripper production. For weak and fractured rocks, definitive geotechnical information has the potential to yield an estimate of the mine site rippability. These parameters usually have some hidden pattern relating them with ripper production for machine being employed. Today, in the era of advanced computing and artificial intelligence, decoding these hidden patterns through machine learning techniques have become a viable option. Therefore, in the present research the rippability estimation was accomplished through several ensemble machine learning techniques based on the five laboratory tested data. Ensemble models like Extra Tree, Gradient Boosting, Histogram Gradient Boosting, AdaBoost, Bagging, and Voting have been developed in the present work. The five geotechnical attributes viz., uniaxial compressive strength, Brazilian tensile strength, slake durability index, point load index, & P-wave velocity have been used as feature variables. The influence on rock mass weathering grade on ripper production has also been investigated in the present research. Additionally, a linear regression model was developed to compare the accuracy of the advanced artificial intelligence models with it. The statistical means to compare the prediction accuracy of the presently developed algorithms are the R2 and mean absolute error (MAE). The Extra Tree regressor beats all other models and achieved a highest R2 and least MAE value among all other algorithms. Comparatively, linear model had displayed statistically impoverished performance in the present research work. © The Institution of Engineers (India) 2025.

关键词： Carbonaceous shale

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Towards sustainable sand resource management - Best governance practices and alternatives to river sand in India

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Environmental Science and Pollution Research 2025年第21期32卷 12551-12578页

作者： Bhatawdekar, Ramesh Murlidhar Mohamad, Edy Tonnizam Singh, Trilok Nath Chengong, Dato Hock Soon Kainthola, Ashutosh Khandelwal, Manoj Chihi, Feten Rathinasamy, Vynotdni Arthur, Clement Kweku Deshpande, Anand Ravi Nigam, Sanjay Kwon, Sangki Ralegaonkar, Rahul Azad, Md Alquamar Universiti Teknologi Malaysia Johor Bahru Skudai Malaysia Deparment of Mining Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Bihar Patna India Malaysia Quarry Association Selangor Kuala Lumpur Malaysia Geoengineering & Computing Laboratory Department of Geology Banaras Hindu University Varanasi India Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3350 Australia Faculty of Civil Engineering Budapest University of Technology and Economics Budapest Hungary School of Civil Engineering Universiti Sains Malaysia Penang Malaysia Department of Mining Engineering Faculty of Mining and Minerals Technology University of Mines and Technology Western Region Tarkwa Ghana SURU09 Business Services Thane India Department of Energy Resources Engineering Inha University Incheon Korea Republic of Department of Civil Engineering Visvesvaraya National Institute of Technology Nagpur India

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Prediction of tunnel ground deformation – A case study from Western Himalaya, India

Results in Earth Sciences

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Results in Earth Sciences 2025年 3卷

作者： A. Ahmed Sudipta K. Mishra Md Alquamar Azad TN Singh Abdullah Ansari Ashutosh Kainthola Shafat Ahmad Khansa Zaidi Intercontinental Consultants & Technocrats Pvt. Ltd India GD Goenka University Gurgaon India Geoengineering and Computing Laboratory Department of Geology Institute of Eminence Banaras Hindu University Varanasi UP India Department of Civil & Environmental Engineering IIT Patna Patna Bihar India Earthquake Monitoring Center Sultan Qaboos University PC: 123 Al Khoudh Muscat Oman Mashreq Global Network Bengaluru India PMT Infrascience Pvt. Ltd Delhi India

This study focuses on prediction and analysis of ground deformation during construction of Khari-Banihal Railway Tunnel (KBRT) in the Western Himalayas. KBRT is located in a geologically disturbed area and has undergone multiple phases of deformation. Objective of this study is to identify the key factors influencing tunnel deformation and develop a predictive model using real-time 3D monitoring data and multiple linear regression (MLR) techniques. Motivation behind this study arises from frequent discrepancies observed between anticipated and actual deformation in tunnels, particularly in geologically complex regions with high tectonic activity. The findings suggest that deformation in tunnel walls and crown is influenced by overburden thickness and in-situ stress. Increasing number of support elements and support pressure leads to higher deformation, while longer pull length reduces deformation tendencies. Size of plastic zone around the tunnel also plays a crucial role in deformation. The proposed predictive model improves project planning by enhancing cost and time efficiency.

关键词： Tunnel deformation 3D monitoring Instrumentations Statistical learning Himalaya

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