AquMADE: A GIS-based web application to assess groundwater quality by introducing a risk-based irrigation water quality index (RB-IWQI)

Qureshi, Seyed Amir Muhammad; Ghavami, Seyed Morsal

doi:10.1016/j.envsoft.2024.106009

Cited by 1 publication

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The availability of groundwater is essential for a nation's socioeconomic progress. But because so much groundwater is used for farming worldwide, there's a risk that these vital natural resources will be contaminated and eventually run out [1].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Assessment and Modelling Using Machine Learning

Mishra,

Patel,

Singh

2024

Preprint

View full text Add to dashboard Cite

One of the most essential sources of water for people to drink is groundwater. Several studies on groundwater have been carried out in India. However, the characteristics of groundwater have not been investigated through machine learning (ML tools). There is a need for a defined strategy which would concentrate on a specific part of groundwater management, which means the protection of groundwater from contamination. This study makes use of 97 groundwater samples that were taken from tube wells and dug wells in various places within Ayodhya, Uttar Pradesh, India from the year 2000–2018 groundwater data yearbook. Seven hydro-chemical parameters from each sample were ascertained and compared to the standard values recommended for drinking purposes by the Bureau of Indian Standards (BIS) 10,500:2012. Anticipating the Water Quality Index (WQI) and Water Quality Classification (WQC) so that WQI is a crucial indication for water validity is the difficulty this research aims to solve. Parameter adjustment and optimization are used in this work to increase the accuracy of multiple machine learning ARIMA model, in which the process of forecasting WQI and WQC is performed. The analysis of the proposed algorithms will assist the relevant government agencies in identifying substitute water for consumption in the affected regions.

show abstract

Section: Introductionmentioning

confidence: 99%