Browsing by Author "Patidar, Nitesh"

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    Assessment of traditional rainwater harvesting system in barren lands of a semi-arid region: a case study of Rajasthan (India)
    (Elsevier, 2022-06-25) Yadav, Basant; Patidar, Nitesh; Sharma, Anupma; Panigrahi, Niranjan; Sharma, Rakesh K.; Loganathan, V.; Krishan, Gopal; Singh, Jaswant; Kumar, Suraj; Parker, Alison
    Study region Dudu station, Rajasthan, India Study focus Rainwater harvesting can be used as a method to recharge aquifers. This can happen with a variety of scales and technologies. One such example is shallow infiltration ponds (Chaukas) which recharge groundwater and increase soil moisture facilitating pastureland development. A HYDRUS-1D model was used to estimate potential groundwater recharge. The model was calibrated using field data from 2019 and validated using data from 2020. The time series of Normalized Difference Vegetation Index (NDVI) was derived at annual scale to assess changes in the vegetation cover. New hydrological insights for the region The modeling revealed that an additional 5% of the rainfall depth was being recharged into the groundwater. In addition, the additional soil moisture was allowing natural grass cover to develop, which could be used by the local community as pastureland. These twin benefits that the local communities are realizing could be scaled up beyond Dudu, to India, and worldwide, as many regions have barren land that is slightly sloping, together with permeable soils, which are the only conditions for the construction of Chaukas. These Chauka systems have helped in sustainable water resources management in these water-stressed regions and the additional livelihood support through developed pastures for animal husbandry.
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    Ensemble modelling framework for groundwater level prediction in urban areas of India
    (Elsevier, 2019-11-24) Yadav, Basant; Gupta, Pankaj Kumar; Patidar, Nitesh; Himanshu, Sushil Kumar
    India is facing the worst water crisis in its history and major Indian cities which accommodate about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information theory (MI), genetic algorithm (GA), artificial neural network (ANN) and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data which consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate were pre-processed using mutual information theory, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre-processing predict groundwater levels accurately (R > 85%). It is also evident from the results that the pre-processing techniques enhance the prediction accuracy and results were improved for 66% of the monitored wells. Analysis of various input parameters suggest, inclusion of population growth rate is positively correlated with decrease in groundwater levels. The developed approach in this study for urban groundwater prediction can be useful particularly in cities where lack of pipeline/sewage/drainage lines leakage data hinders physical based modelling.