Geospatial Technology for River Water Quality and Morphological Assessment: A Review of Recent Advances

DOI

10.2991/978-94-6239-791-0_23How to use a DOI?

Keywords

GIS; Water Quality Assessment; machine learning; river Morphology; flood risk; Indian river

Abstract

Urban rivers in India are showing sign of deterioration – murky water, collapsing banks, and increasing in pollution that far exceeds natural recovery rates. Industrial waste, populated cities’ sewage, and agriculture runoffs enter rivers faster than they can recover. Field based monitoring is reliable but cannot be used for whole river basin, it is expensive, slow and fixed to specific points.

Geographic Information System (GIS) systems like GIS, remote sensing and machine learning are used worldwide, but still their applications to peri urban rivers in Maharashtra remain unexplored. This review examines fifteen peer-reviewed papers published between 2022 – 2026, from Scopus and WOS indexed journals; to evaluate how GIS based models are used to assess river water quality and morphological analysis.

Four themes are found: GIS integrated Water Quality Index mapping, machine learning models, geospatial flood risk and basin change monitoring and GIS research on Indian rivers specifically.

WQI- GIS mapping integrated with Principal Component Analysis and Cluster Analysis mostly used approach for finding pollution hotspots across river basin. Machine learning models like Random Forest, XGBoost and LSTM – perform better combined with GIS variables are included. These models show accuracy from 0.91 – 0.93 in recent studies. GIS – AHP mostly used in flood mapping, though Random Forest based model generally gives higher accuracy.

The important gap is found that there is very little work done on Peri urban rivers in Maharashtra and especially rivers from Pune district, despite its significant impact on environment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Janwale Asaram Pandurang
AU  - Mayur Deshmukh
PY  - 2026
DA  - 2026/10/09
TI  - Geospatial Technology for River Water Quality and Morphological Assessment: A Review of Recent Advances
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 299
EP  - 309
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_23
DO  - 10.2991/978-94-6239-791-0_23
ID  - Pandurang2026
ER  -