Abstract
Nowadays, river water is continuously polluting due to rapid urban growth. Hence, measuring the water quality (WQ) year wise is the most needed task for today’s culture. However, usual neural network features are limited to forecasting the WQ that was resulted in poor estimation exactness. So, a novel Dove-based varying coefficient neural network (DbVCNet) was executed to find the WQ of Burhi Gandak river and the land use changes of Muzaffarpur city and their surroundings. Hence, the specific region’s satellite images and WQ data were utilized to attain these objectives. Hence, the WQ was analyzed by recognizing the changes in chemical quantity in river water. In addition, the percentage of land use changes was estimated based on cropland, urban growth and forest region. Subsequently, it was tested in the MATLAB Tool, and robustness was estimated against the recent existing model. A novel DbVCNet scores the widest range of WQ estimation rate at 98.5%, which is the finest exactness range compared to past studies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data sharing IS not applicable to this article as no datasets were generated or analyzed during the current study.
Change history
27 June 2023
A Correction to this paper has been published: https://doi.org/10.1007/s13762-023-05061-x
References
Ahmad MN, Shao Z, Javed A, Islam F, Ahmad HH, Aslam RW (2023) the cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan. Photogramm Eng Remote Sens 89(1):47–55. https://doi.org/10.14358/PERS.22-00102R2
Akdeniz HB, Sag NS, Inam S (2023) Analysis of land use/land cover changes and prediction of future changes with land change modeler: case of Belek. Turkey Environ Monit Assess 195(1):135. https://doi.org/10.1007/s10661-022-10746-w
AlDousari AE, Kafy AA, Saha M, Fattah MA, Almulhim AI, Faisal AA, Rakib AA, Jahir DMA, Rahaman ZA, Bakshi A, Shahrier M, Rahman MM (2022) Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait. Sustain Cities Soc 86:104107. https://doi.org/10.1016/j.scs.2022.104107
Assede ESP, Orou H, Biaou SSH, Geldenhuys CJ, Ahononga FC, Chirwa PW (2023) Understanding drivers of land use and land cover change in Africa: a review. Curr Landscape Ecol Rep. https://doi.org/10.1007/s40823-023-00087-w
Bellot A, Dhir A, Prando G (2022) Generalization bounds and algorithms for estimating conditional average treatment effect of dosage. arXiv preprint arXiv:2205.14692. https://doi.org/10.48550/arXiv.2205.14692
Boulila W, Ghandorh H, Khan MA, Ahmed F, Ahmad J (2021) A novel CNN-LSTM-based approach to predict urban expansion. Ecol Inform 64:101325. https://doi.org/10.1016/j.ecoinf.2021.101325
Chanapathi T, Thatikonda S (2020) Investigating the impact of climate and land-use land cover changes on hydrological predictions over the Krishna river basin under present and future scenarios. Sci Total Environ 721:137736. https://doi.org/10.1016/j.scitotenv.2020.137736
Chen X, Yu L, Cao Y, Xu Y, Zhao Z, Zhuang Y, Liu X, Du Z, Liu T, Yang B, Gong P (2023) Habitat quality dynamics in China’s first group of national parks in recent four decades: Evidence from land use and land cover changes. J Environ Manage 325:116505. https://doi.org/10.1016/j.jenvman.2022.116505
de Oliveira Serrão EA, Silva MT, Ferreira TR, de Ataide LCP, dos Santos CA, de Lima AMM, da Silva VPR, de Sousa FAS, Gomes DJC (2022) Impacts of land use and land cover changes on hydrological processes and sediment yield determined using the SWAT model. Int J Sediment Res 37(1):54–69. https://doi.org/10.1016/j.ijsrc.2021.04.002
Gupta K, Ganesh K, Parmar U, Vinod S (2022) Water resource detection using satellite images. Int Res J Mod Eng Technol Sci 4(7):985–990
Huang Z, Qi H, Kang C, Su Y, Liu Y (2020) An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data. Remote Sens 12(19):3254. https://doi.org/10.3390/rs12193254
Hussain S, Mubeen M, Karuppannan S (2022) Land use and land cover (LULC) change analysis using TM, ETM+ and OLI Landsat images in district of Okara, Punjab, Pakistan. Phys Chem Earth, Parts A/B/C 126:103117. https://doi.org/10.1016/j.pce.2022.103117
Jardim AMRF, Araújo Júnior GN, Silva MV, Santos A et al (2022) Using remote sensing to quantify the joint effects of climate and land use/land cover changes on the Caatinga biome of Northeast Brazilian. Remote Sens 14(8):1911. https://doi.org/10.3390/rs14081911
Khan MSI, Islam N, Uddin J, Islam S, Nasir MK (2022) Water quality prediction and classification based on principal component regression and gradient boosting classifier approach. J King Saud Univ Comput Inf Sci 34(8):4773–4781. https://doi.org/10.1016/j.jksuci.2021.06.003
Nath N, Sahariah D, Meraj G, Debnath J, Kumar P et al (2023) Land use and land cover change monitoring and prediction of a UNESCO world heritage site: Kaziranga eco-sensitive zone using cellular automata-Markov model. Land 12(1):151. https://doi.org/10.3390/land12010151
Rahaman ZA, Kafy AA, Faisal AA, Al Rakib A, Jahir DMA, Fattah MA, Kalaivani S, Rathi R, Mallik S, Rahman MT (2022) Predicting microscale land use/land cover changes using cellular automata algorithm on the northwest coast of peninsular Malaysia. Earth Syst Environ 6(4):817–835. https://doi.org/10.1007/s41748-022-00318-w
Seyam MMH, Haque MR, Rahman MM (2023) Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: a case study at Bhaluka in Mymensingh, Bangladesh. Case Stud Chem Environ Eng 7:100293. https://doi.org/10.1016/j.cscee.2022.100293
Sharma K, Garg A, Joshi V, Kumar A (2022) Assessment of health risks for criteria air pollutants present in 11 non-attainment cities of Uttar Pradesh, India. Hum Ecol Risk Assess. https://doi.org/10.1080/10807039.2022.2157242
Shurtz KM, Dicataldo E, Sowby RB, Williams GP (2022) Insights into efficient irrigation of urban landscapes: analysis using remote sensing, parcel data, water use, and tiered rates. Sustainability 14(3):1427. https://doi.org/10.3390/su14031427
Su MC, Chen JH, Utami AM, Lin SC, Wei HH (2022) Dove swarm optimization algorithm. IEEE Access 10:46690–46696. https://doi.org/10.1109/ACCESS.2022.3170112
Thamaga KH, Dube T, Shoko C (2022) Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa. Geocarto Int 37(20):5891–5913. https://doi.org/10.1080/10106049.2021.1926552
Verma P, Singh PK, Sinha RR, Tiwari AK (2020) Assessment of groundwater quality status by using water quality index (WQI) and geographic information system (GIS) approaches: a case study of the Bokaro district, India. Appl Water Sci 10:1–16. https://doi.org/10.1007/s13201-019-1088-4
Vu HTD, Tran DD, Schenk A, Nguyen CP, Vu HL, Oberle P, Trinh VC, Nestmann F (2022) Land use change in the Vietnamese Mekong Delta: new evidence from remote sensing. Sci Total Environ 813:151918. https://doi.org/10.1016/j.scitotenv.2021.151918
Wang SW, Munkhnasan L, Lee WK (2021) Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environ Chall 2:100017. https://doi.org/10.1016/j.envc.2020.100017
Wang M, Wander M, Mueller S, Martin N, Dunn JB (2022) Evaluation of survey and remote sensing data products used to estimate land use change in the United States: evolving issues and emerging opportunities. Environ Sci Policy 129:68–78. https://doi.org/10.1016/j.envsci.2021.12.021
Weslati O, Bouaziz S, Sarbeji MM (2023) Modelling and assessing the spatiotemporal changes to future land use change scenarios using remote sensing and CA-markov model in the mellegue catchment. J Indian Soc Remote Sens 51(1):9–29. https://doi.org/10.1007/s12524-022-01618-4
Xing W, Qian Y, Guan X, Yang T, Wu H (2020) A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Comput Geosci 137:104430. https://doi.org/10.1016/j.cageo.2020.104430
Yao Y, Yan X, Luo P, Liang Y, Ren S, Hu Y, Han J, Guan Q (2022) Classifying land-use patterns by integrating time-series electricity data and high-spatial resolution remote sensing imagery. Int J Appl Earth Obs Geoinf 106:102664. https://doi.org/10.1016/j.jag.2021.102664
Yao S, Chen C, He M, Cui Z, Mo K, Pang R, Chen Q (2023) Land use as an important indicator for water quality prediction in a region under rapid urbanization. Ecol Indic 146:109768. https://doi.org/10.1016/j.ecolind.2022.109768
Yin J, Dong J, Hamm NAS, Li Z, Wang J, Xing H, Fu P (2021) Integrating remote sensing and geospatial big data for urban land use mapping: a review. Int J Appl Earth Obs Geoinf 103:102514. https://doi.org/10.1016/j.jag.2021.102514
Zhao F, Yang L, Tang J, Fang L, Yu X, Li M et al (2023) Urbanization–land-use interactions predict antibiotic contamination in soil across urban–rural gradients. Sci Total Environ 867:161493. https://doi.org/10.1016/j.scitotenv.2023.161493
Acknowledgements
None
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no potential conflict of interest.
Ethical approval
All applicable institutional and/or national guidelines for the care and use of animals were followed.
Informed consent
For this type of study, formal consent is not required.
Additional information
Editorial responsibility: Chongqing Wang.
Original version of this article corrected for first author name.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Arman Ali, M., Roy, L.B. Muzaffarpur city land changes and impact on urban runoff and water quality of the river Burhi Gandak. Int. J. Environ. Sci. Technol. 21, 2071–2082 (2024). https://doi.org/10.1007/s13762-023-05008-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-023-05008-2