Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this pro...

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Vydané v:	Journal of chemistry Ročník 2022; s. 1 - 17
Hlavní autori:	Dimple, Dimple, Rajput, Jitendra, Al-Ansari, Nadhir, Elbeltagi, Ahmed
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Hindawi 29.08.2022 John Wiley & Sons, Inc Wiley
Predmet:	Agriculture Algorithms Aquatic resources Artificial intelligence California Case studies Chemical oxygen demand Correlation coefficients Crop production Crop yield Data mining Developing countries Error reduction Fresh water Geoteknik Global positioning systems GPS Groundwater Groundwater quality India Irrigation Irrigation water LDCs Machine learning Magnesium Management Mathematical models Model accuracy Neural networks Parameters Permeability Quality assessment Rivers Root-mean-square errors Semi arid environments Sodium Soil Mechanics Support vector machines Surface water Water Water management Water quality Water resources Water, Underground California India
ISSN:	2090-9063, 2090-9071, 2090-9071
On-line prístup:	Získať plný text
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