Dataset on the evaluation of hydrochemical properties and groundwater suitability for irrigation purposes: South-western part of Jashore, Bangladesh

Hasan, Yeasir; Khan, Mohammad Forrukh Hossain; Islam, Kamrul; Hasan, Mehedi; Hossain, Alam; Monir, Minhaj Uddin; Samad, Abdus; Ahmed, Mohammad Tofayal

doi:10.1016/j.dib.2020.106315

Cited by 7 publications

(1 citation statement)

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“…It takes into consideration the individual contribution and relative weight of each water parameter when classifying water samples for irrigation [29]. There are various approaches to calculating IWQI, notable among which are the WQI approach proposed by WHO (as used in [10], [24], [31]) and that of Meireles et al [29]. For this work, we stick to WHO's approach, which we also use for assessing the quality of drinking water.…”

Section: ) Assessing Water Quality For Irrigationmentioning

confidence: 99%

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

et al. 2022

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Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting. To this end this study proposes a network architecture to collect data on water parameters in real-time and use machine learning (ML) tools to automatically determine suitability of water samples for drinking and irrigation purposes. The developed monitoring network is based on LoRa and takes the land topology into consideration. Results of simulations done in Radio Mobile revealed a partial mesh network topology as the most adequate. Due to the absence of large and open datasets on drinking and irrigation water, datasets usable for training ML models were developed. Three ML models -Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) were considered for the water classification and results obtained showed that LR performed best for drinking water, while SVM was better suited for irrigation water. Recursive feature elimination was then combined with the three ML models to reveal which of the water parameters had the greatest influence on the classification accuracies of the respective model.

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