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- W3144098420 abstract "Water is the elixir of life. Gradual increase in demand for water, whether for agricultural, personal or industrial use has led to the depletion of clean water resources. Hence, to battle this water crisis, we propose to build a model using machine learning. The goal of the model is to predict the groundwater levels of different areas in Delhi with respect to various parameters that affect the groundwater levels in the environment. To find the best machine learning technique to perform the prediction of groundwater, we initially perform a comparative analysis of four machine learning techniques: Support vector machine, Artificial neural networks, Random forests and Linear regression models. We proceed with the models that give satisfactory results. We perform forecasting using the ARIMA model to predict the parameters that affect groundwater levels across the next ten years. The forecasting of parameters resulted in increasing the database by two times, hence we performed the prediction of the water table using the models Support vector machine, Artificial neural networks and Linear regression models. Thus, concluded that the Artificial neural networks model performs better than the Support vector machine and Linear regression models. It presented with a mean absolute error of 85.26 as opposed to linear regression model that presented with a mean absolute error of 94.95 and Support vector machine with a mean absolute error of 127.74. Further these predictions are used to build optimized groundwater distribution routes using the optimization algorithm vehicle routing problem. If these routes are implemented using proper infrastructure, we can provide the resource of groundwater to the areas that are experiencing scarcity of water." @default.
- W3144098420 created "2021-04-13" @default.
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- W3144098420 date "2020-01-01" @default.
- W3144098420 modified "2023-09-25" @default.
- W3144098420 title "Analysis and Optimization of Groundwater Distribution Using SVM and Neural Networks" @default.
- W3144098420 doi "https://doi.org/10.2139/ssrn.3606897" @default.
- W3144098420 hasPublicationYear "2020" @default.
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