Abstract
In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012–2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012–2013, whereas ANN predicted results of 2023–2024 predict no rise in water level > 4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
Will be provided as per the requirement of Editor/ Reviewer.
References
Anastasiadisa AD, Magoulasa GD, Vrahatis MN (2005) New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 64:253–270
Annapoorna H, Janardhana MR (2015) Assessment of Groundwater Quality for Drinking Purpose in Rural Areas Surrounding a Defunct Copper Mine. Aquat Procedia 4:685–692
Asoka A, Gleeson T, Wada Y, Mishra V (2017) Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India. Nat Geosci 10:109–117
Bărbulescu A (2016) Studies on Time Series Applications in Environmental Sciences. Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-319-30436-6
Bhunia GS, Keshavarzi A, Shit PK, Omran ESE, Bagherzadeh A (2018) Evaluation of groundwater quality and its suitability for drinking and irrigation using GIS and geostatistics techniques in semiarid region of Neyshabur. Iran Appl Water Sci 8:168
Brown RG (1956) Exponential Smoothing for Predicting Demand. Arthur D. Little Inc, Cambridge, Massachusetts, p 15
Central Ground Water Board (2017) Report on aquifer mapping and ground water management, Jaipur District. Ministry of Water Resources, River Development & Ganga Rejuvenation, Western Region Jaipur, Rajasthan Government of India.
Charulatha G, Srinivasalu S, Maheswari OU, Venugopal T, Giridharan L (2017) Evaluation of ground water quality contaminants using linear regression and artificial neural network models. Arab J Geosci 10:128
Csábrági A, Molnár S, Tanos P, Kovács J (2017) Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river Danube. Ecol Eng 100:63–72
Gautam SK, Tziritis E, Singh SK, Tripathi JK, Singh AK (2018) Environmental monitoring of water resources with the use of PoS index: a case study from Subarnarekha River basin. India Environ Earth Sci 77:70
Gharbia AS, Gharbia SS, Abushbak T, Wafi H, Aish A, Zelenakova M, Pilla F (2016) Groundwater Quality Evaluation Using GIS Based Geostatistical Algorithms. J Geosci Environ Prot 4(2):89–103
Holt CC (1957) Forecasting Trends and Seasonals by Exponentially Weighted Averages, Carnegie Institute of Technology, Pittsburgh Office of Naval Research memorandum no. 52.
Hyndman RJ, O'Hara-Wild M, Bergmeir C, Razbash S, Wang E (2017) Forecast: Forecasting functions for time series and linear models. R package version 8.2. https://CRAN.Rproject.org/package=forecast
Jain N (2016) Physio-chemical profile of drinking water of several villages of Jaipur district with special refrence to Dental fluorosis. Ecoscan 10(1–2):185–190
Judge GG, Hill RC, William EG, Helmut I (1988) Introduction to the Theory and Practice of Econometrics. 2nd ed., John Wiley and Son, INC. New York, Toronto, Singapore
Khan UT, He J, Valeo C (2018) River flood prediction using fuzzy neural networks: an investigation on automated network architecture. Water Sci Technol 2017 1:238–247
Kisi O, Azad A, Kashi H (2019) Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods. Water Resour Manage 33:847–861
Lee S, Lee KK, Yoon H (2019) Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeol J 27:567–579
Machado RMA, Serralheiro RP (2017) Soil Salinity: Effect on Vegetable Crop Growth. Management Practices to Prevent and Mitigate Soil Salinization. Horticulturae 3 (2): 30
Measho S, Chen B, Trisurat Y, Pellikka P, Guo L, Arunyawat S, Tuankrua V, Ogbazghi W, Yemane T (2019) Spatio-temporal analysis of vegetation dynamics as a response to climate variability and drought patterns in the semiarid region, Eritrea. Remote Sens 11(6):724
Natarajan N, Sudheer Ch (2020) Groundwater level forecasting using soft computing techniques. Neural Comput Appl 32(12):7691–7708
Rabeiy RE (2018) Assessment and modeling of groundwater quality using WQI and GIS in Upper Egypt area. Environ Sci Pollut Res 25:30808–30817
Ramakrishnaiah CR, Sadashivaiah C, Ranganna G (2009) Assessment of water quality index for the groundwater in Tumkar Taluk, Karnataka State. India E-J Chem 6(2):523–530
Rawat KS, Singh SK, Gautam SK (2018) Assessment of groundwater quality for irrigation use: a peninsular case study. Appl Water Sci 8:233
Ruybal CJ, Hogue TS, McCray JE (2019) Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging. Water Resour Res 55(4):2820–2837
Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US. Water Resour Res 53(5):3878–3895
Sharma SK, Sharma U, Chandel CPS (2015) Qualitative aspects of Ground water for drinking purpose of Phagi Block of Jaipur District (Rajasthan). Ascent Int J Res Analysis 2(2): 57.1- 57.9
Shrivastava R, Chug S, Lora CP, Meena M (2019) Study of Physio-Chemical Parameters and Quality Assessment of Ground Water of District Jaipur, Rajasthan. India Adv Sci Eng Med 11(1–2):155–157
Singh P, Fahmi N, Singh VP, Singh RV (2012) Studies on Ground Water Fluoride Content and Water quality in Phagi Tehsil of Jaipur District. Int J Chem Environ Pharm Res 3(3):219–224
Singh SK, Srivastava PK, Pandey AC, Gautam SK (2013) Integrated assessment of groundwater influenced by a confluence river system: concurrence with remote sensing and geochemical modelling. Water Resour Manag 27(12):4291–4313
Souissi D, Msaddek MH, Zouhri L, Chenini I, May ME, Dlala M (2018) Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches, southeast Tunisia. Hydrol Sci J 63(2):251–268
Sunayana KK, Dube O, Sharma R (2019) Use of neural networks and spatial interpolation to predict groundwater quality. Environ Dev Sustain 22:2801–2816
Tapoglou E, Karatzas GP, Trichakis IC, Varouchakis EA (2014) A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation. J Hydrol 519(PD): 3193–3203
Thomas BF, Famiglietti JS (2019) Identifying Climate-Induced Groundwater Depletion in GRACE Observations. Sci Rep 9:4124
WHO (2017) Guidelines for drinking water quality: training pack, 4th edn. Incorporating The First Addendum, Geneva, Switzerland
Xiang SL, Liu ZM, Ma LP (2006) Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Sci 24(1):60–62
Zanotti C, Rotiroti M, Fumagalli L, Stefania GA, Canonaco F, Stefenelli G, Prévôt ASH, Leoni B, Bonomi T (2019) Groundwater and surface water quality characterization through positive matrix factorization combined with GIS approach.Water Res 159:122–134
Zhou Y, Herath HMPSD (2017) Evaluation of alternative conceptual models for groundwater modelling. Geosci Front 8(3):437–443
Zörb C, Geilfus CM, Dietz KJ (2019) Salinity and crop yield. Plant Biol 21(S1):31–38
Acknowledgements
Authors acknowledge Department of Science & Technology, Government of India for financial support vide reference number DST/WOS-B/2018/1575/ETD/Ankita under Women Scientist Scheme (WOS-B) to carry out this research work.
Funding
Department of Science & Technology, Government of India for financial support vide reference number DST/WOS-B/2018/1575/ETD/Ankita under Women Scientist Scheme (WOS-B) to carry out this research work.
Author information
Authors and Affiliations
Contributions
All authors contributed to this manuscript. Dr. Ankita Pran Dadhich: Conceived and designed the analysis; performed analysis; wrote the manuscript. Dr. Rohit Goyal: Contributed in analysis tools and provided inputs in writing the manuscript. Dr. Pran Nath Dadhich: Conceived & Collected data and provided inputs in writing the manuscript.
Corresponding author
Ethics declarations
Consent to Participate
This manuscript in part or in full has not been submitted or published anywhere. The manuscript will not be submitted elsewhere until the editorial/review process is completed.
Consent to Publish
Hereby all authors agree for publication of this manuscript in WARM.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dadhich, A.P., Goyal, R. & Dadhich, P.N. Assessment and Prediction of Groundwater using Geospatial and ANN Modeling. Water Resour Manage 35, 2879–2893 (2021). https://doi.org/10.1007/s11269-021-02874-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-021-02874-8