Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Sushant K. Singh, Nadhir Al-Ansari et al., 2020. Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Int. J. Enviorn. Res. Publ. Health, 10.3390/ijerph17082749. CrossRef | Direct Link |
Singh, S.K., 2020. Global decision support dashboard of covid-19. AIMS Med. Sci., 7: 40-42. CrossRef | Direct Link |
Singh, S.K., 2020. Covid-19: a master stroke of nature. AIMS Public Health, 7: 393-402. CrossRef | Direct Link |
Singh, S.K. and R.W. Taylor, 2020. Likelihood of adoption of arsenic-mitigation technologies under perceived risks to health, income, and social discrimination to arsenic contamination. Proceedings of the 7th International Congress and Exhibition on Arsenic in the Environment (AS 2018), July 1-6, 2018 CRC Press pp: 525-529. CrossRef | Direct Link |
Pham, T.B., S.K. Singh and H.B. Ly, 2020. Using artificial neural network (ANN) for prediction of soil coefficient of consolidation. Vietnam J. Earth Sci., 10.15625/0866-7187/42/4/15008. CrossRef | Direct Link |
Pham, B.T., T.V. Phong, T. Nguyen-Thoi, P.T. Trinh and Q.C. Tran et al., 2020. GIS-based ensemble soft computing models for landslide susceptibility mapping. Adv. Space Res., 66: 1303-1320. CrossRef | Direct Link |
Pham, B.T., T.V. Phong, T. Nguyen-Thoi, K. Parial and S.K. Singh et al., 2020. Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int., 10.1080/10106049.2020.1737972. CrossRef | Direct Link |
Nhu, V.H., D. Zandi, H. Shahabi, K. Chapi and A. Shirzadi et al., 2020. Comparison of support vector machine, bayesian logistic regression, and alternating decision tree algorithms for shallow landslide susceptibility mapping along a mountainous road in the west of Iran. Appl. Sci., 10.3390/app10155047. CrossRef | Direct Link |
K, S.S., 2020. A commentary on the application of artificial intelligence in the insurance industry. Trends Artif. Intell., 10.36959/643/305. CrossRef | Direct Link |
Bui, D.T., A. Shirzadi, A. Amini, H. Shahabi and N. Al-Ansari et al., 2020. A hybrid intelligence approach to enhance the prediction accuracy of local scour depth at complex bridge piers. Sustainability, 10.3390/su12031063. CrossRef | Direct Link |
Singh, S.K., 2019. A career in environmental informatics and environmental data science. Front. Ecol. Environ., 17: 240-241. CrossRef | Direct Link |
Singh, S.K. and R.W. Taylor, 2019. Assessing the role of risk perception in ensuring sustainable arsenic mitigation. Groundwater Sustainable Dev., 10.1016/j.gsd.2019.100241. CrossRef | Direct Link |
Singh, S.K. and R.W. Taylor, 2019. Assessing and mapping human health risks due to arsenic and socioeconomic correlates for proactive arsenic mitigation. In: Arsenic Water Resources Contamination, Fares A. and S. Singh, Springer International Publishing Switzerland, ISBN-13 978-3-030-21257-5, Pages: 26. CrossRef | Direct Link |
Phong, T.V., T.T. Phan, I. Prakash, S.K. Singh and A. Shirzadi et al., 2019. Landslide susceptibility modeling using different artificial intelligence methods: a case study at muong lay district, Vietnam. Geocarto Int., 10.1080/10106049.2019.1665715. CrossRef | Direct Link |
Pham, B.T., I. Prakash, S.K. Singh, A. Shirzadi and H. Shahabi et al., 2019. Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: hybrid machine learning approaches. CATENA, 175: 203-218. CrossRef | Direct Link |
Pham, B.T., I. Prakash, J. Dou, S.K. Singh and P.T. Trinh et al., 2019. A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int., 35: 1267-1292. CrossRef | Direct Link |
Pham, B.T., A. Shirzadi, H. Shahabi, E. Omidvar and S.K. Singh et al., 2019. Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability, 10.3390/su11164386. CrossRef | Direct Link |
Pham, B.T., A. Jaafari, I. Prakash, S.K. Singh, N.K. Quoc and D.T. Bui, 2019. Hybrid computational intelligence models for groundwater potential mapping. CATENA, 10.1016/j.catena.2019.104101. CrossRef | Direct Link |
Fares, A. and S.K. Singh, 2019. Arsenic Water Resources Contamination. Springer, Cham, Switzerland, . CrossRef | Direct Link |
Singh, S.K., R.W. Taylor, M.M. Rahman and B. Pradhan, 2018. Developing robust arsenic awareness prediction models using machine learning algorithms. J. Environ. Manage., 211: 125-137. CrossRef | Direct Link |
Menapace, M., 2018. Scientific ethics: a new approach. Int. J. Appl. Sci. - Res. Rev., 10.21767/2394-9988-c1-002. CrossRef | Direct Link |
Chakraborti, D., S. Singh, M. Rahman, R. Dutta and S. Mukherjee et al., 2018. Groundwater arsenic contamination in the Ganga river basin: a future health danger. Int. J. Environ. Res. Public Health, 10.3390/ijerph15020180. CrossRef | Direct Link |
Singh, S.K., S.A. Brachfeld and R.W. Taylor, 2016. Evaluating Hydrogeological and Topographic Controls on Groundwater Arsenic Contamination in the Mid-Gangetic Plain in India: Towards Developing Sustainable Arsenic Mitigation Models. In: Advances in Water Security: Emerging Sensing Issues for Coastal Groundwater Quality and Quantity, Fares, A. (Ed.). Springer International Publishing, New York, USA.
Singh, S.K. and N. Vedwan, 2015. Mapping composite vulnerability to groundwater arsenic contamination: An analytical framework and a case study in India. Nat. Hazards, 75: 1883-1908. CrossRef | Direct Link |
Siegel, P.E., J.G. Jones, D.M. Pearsall, N.P. Dunning and P. Farrell et al., 2015. Paleoenvironmental evidence for first human colonization of the Eastern Caribbean. Quat. Sci. Rev., 129: 275-295. CrossRef | Direct Link |
Singh, S.K., D.S. Gean and K.P. Srikanta, 2014. Multiple groundwater contamination in the Mid-Gangetic Plain, Bihar (India): A potential threat. Int. J. Adv. Res. Sci. Tech., 3: 175-179.
Singh, S.K., C. Feldman and S. Wunderlich, 2014. Heavy metal contamination in vegetables grown in an urban community garden in the Northeast USA: A preliminary study. Food Stud. Interdiscip. J., 3: 77-87.
Singh, S.K., C. Feldman and S. Wunderlich, 2014. Disaster issues and management in farm and urban crop production. Perspect. Public Health, 134: 127-128.
Singh, S.K., A.K. Ghosh, A. Kumar, K. Kislay and C. Kumar et al., 2014. Groundwater arsenic contamination and associated health risks in Bihar, India. Int. J. Environ. Res., 8: 49-60. Direct Link |
Singh, S.K., 2014. Groundwater arsenic contamination in the middle-gangetic plain, bihar (India): The danger arrived. Int. Res. J. Environ. Sci., 4: 70-76.
Singh, S.K. and A.K. Ghosh, 2012. Health risk assessment due to groundwater arsenic contamination: Children are at high risk. Hum. Ecol. Risk Assess. Int. J., 18: 751-766. CrossRef | Direct Link |
Singh, S.K., 2011. Arsenic Contamination in Water, Soil and Food Materials in Bihar. Lambert Academic Publishing, Germany.
Singh, S.K. and A.K. Ghosh, 2011. Entry of arsenic into food material-a case study. World Applied Sci. J., 13: 385-390. Direct Link |
Singh, S.K. and A.K. Ghosh, 2010. Effect of arsenic on photosynthesis, growth and its accumulation in the tissues of Allium cepa (Onion). Int. J. Environ. Eng. Manage., 1: 39-50.
Ghosh, A.K., S.K. Singh, N. Bose, S.K. Singh, N.P. Roy and A. Upadhyaya, 2009. Arsenic Hot Spots Detected in the State of Bihar (India) a Serious Health Hazards for Estimated Human Population of 5.5 Lakh. In: Assessment of Ground Water Resources and Management, Ramanathan, A.L., P. Bhattacharya, P.K. Keshari, J. Bundschuh, D. Chandrashekharam and S.K. Singh (Eds.). IK International Publishing House Pvt. Ltd., New Delhi, India, pp: 62-70.