Sepideh Zeraati Neyshabouri; Abbas Khashei-Siuki; Mohammad Ghasem Akbari

doi:10.26599/JGSE.2026.9280074

doi: 10.26599/JGSE.2026.9280074

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出版历程
- 收稿日期: 2025-05-15
- 录用日期: 2025-11-16
- 网络出版日期: 2026-01-15
- 刊出日期: 2026-03-15

A state-of-the-art Fuzzy Nonlinear Additive Regression (FNAR) model for groundwater level prediction

1.
Department of Water Engineering, University of Birjand, Birjand, Iran
2.
Department of Mathematical Sciences, University of Birjand, Birjand, Iran
3.
Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

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Corresponding author: abbaskhashei@yahoo.com

摘要

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Abstract: Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems, necessitating endeavors to quantify Groundwater Levels (GWL) dynamics to inform policymakers and hydrogeologists. This study introduces a novel Fuzzy Nonlinear Additive Regression (FNAR) model to predict monthly GWL in an unconfined aquifer in eastern Iran, using a 19-year (1998–2017) dataset from 11 piezometric wells. Under three distinct scenarios with progressively increasing input complexity, the study utilized readily available climate data, including Precipitation (Prc), Temperature (Tave), Relative Humidity (RH), and Evapotranspiration (ETo). The dataset was split into training (70%) and validation (30%) subsets. Results showed that among three input scenarios, Scenario 3 (Sc3, incorporating all four variables) achieved the best predictive performance, with RMSE ranging from 0.305 m to 0.768 m, MAE from 0.203 m to 0.522 m, NSE from 0.661 to 0.980, and PBIAS from 0.771% to 0.981%, indicating low bias and high reliability. However, Sc2 (excluding ETo) with RMSE ranging from 0.4226 m to 0.9909 m, MAE from 0.3418 m to 0.8173 m, NSE from 0.2831 to 0.9674, and PBIAS from −0.598% to 0.968% across different months offers practical advantages in data-scarce settings. The FNAR model outperforms conventional Fuzzy Least Squares Regression (FLSR) and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical. Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.
- Birjand aquifer /
- Data-scarce regions /
- Fuzzy-based approach /
- Groundwater table /
- Novel statistical model /
- Soft computing

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Figure 1. Geographic and topographic context of the Birjand aquifer study area

Notes: Location of South Khorasan Province within Iran (Top right); Political divisions of South Khorasan with Birjand Basin highlighted in pink (Bottom right); Digital elevation model (DEM) of the Birjand basin (elevation range: 1,172–2,729 m above sea level), with the unconfined aquifer extent shown in blue (Main panel). Coordinate system: WGS84/UTM Zone 40N. Scale bar and north arrow included for spatial reference.

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Figure 2. Boxplot of monthly meteorological variables (Tave, RH, Prc, ETo) at Birjand station.

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Figure 3. Flowchart of the Proposed FNAR Model for Estimating Monthly GWL.

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Figure 4. Comparative analysis of monthly GWL values performance of FNAR and FLSR models

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Table 1. Input parameter combinations for GWL estimation in model scenarios of the Birjand Plain

Scenario number	Climatic parameters
Sc 1	Prc, T_ave
Sc 2	Prc, T_ave RH
Sc 3	Prc, T_ave RH, ETo
Notes: This table provides an overview of the input variables employed in the model scenarios for GWL estimation. These variables include key climatic factors such as average precipitation (Prc), mean air temperature (T_ave), relative humidity (RH), and evapotranspiration (ETo).

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Table 2. Validation indices for FNAR model performance in monthly GWL estimation under different scenarios

Model/Sc	Month	Training			Testing
Model/Sc	Month	RMSE (m)	MAE	NSE	RMSE (m)	MAE	NSE
FNAR/Sc1	January	1.115	0.879	0.761	1.051	0.531	0.762
	February	1.181	0.955	0.738	1.124	0.901	0.831
	March	1.310	1.074	0.665	1.361	1.101	0.710
	April	1.150	0.653	0.813	1.061	0.403	0.806
	May	1.545	1.103	0.316	1.525	0.998	0.325
	June	1.195	0.649	0.730	1.159	0.488	0.780
	July	1.182	0.633	0.737	1.061	0.403	0.806
	August	1.312	0.741	0.629	1.356	0.757	0.611
	September	1.236	1.059	0.692	1.265	1.073	0.694
	October	1.406	0.912	0.473	1.470	0.831	0.400
	November	1.449	1.259	0.614	1.421	1.124	0.656
	December	1.205	0.793	0.620	1.323	0.738	0.617
FNAR/Sc2	January	0.423	0.353	0.966	0.433	0.342	0.806
	February	0.549	0.415	0.943	0.553	0.509	0.774
	March	0.564	0.451	0.937	0.576	0.486	0.910
	April	0.540	0.445	0.943	0.490	0.450	0.809
	May	0.941	0.794	0.826	0.991	0.817	0.841
	June	0.650	0.527	0.919	0.588	0.499	0.729
	July	0.985	0.740	0.721	0.769	0.535	0.522
	August	0.934	0.673	0.836	0.433	0.342	0.806
	September	0.813	0.793	0.493	0.618	0.529	0.702
	October	0.683	0.596	0.914	0.952	0.670	0.283
	November	0.926	0.705	0.742	0.825	0.803	0.470
	December	0.682	0.390	0.967	0.800	0.612	0.519
FNAR/Sc3	January	0.324	0.248	0.980	0.305	0.258	0.926
	February	0.400	0.324	0.969	0.337	0.203	0.910
	March	0.457	0.402	0.821	0.586	0.522	0.719
	April	0.392	0.285	0.970	0.312	0.331	0.927
	May	0.768	0.291	0.872	0.600	0.272	0.709
	June	0.428	0.390	0.964	0.646	0.413	0.661
	July	0.526	0.450	0.948	0.488	0.368	0.815
	August	0.511	0.434	0.931	0.497	0.409	0.810
	September	0.585	0.519	0.936	0.466	0.390	0.831
	October	0.555	0.431	0.943	0.510	0.330	0.794
	November	0.460	0.349	0.957	0.310	0.333	0.925
	December	0.549	0.415	0.943	0.305	0.258	0.926

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Table 3. Validation indices for FLSR model performance in monthly GWL estimation under different scenarios

Regression Model/Sc	Month	Training			Testing
Regression Model/Sc	Month	RMSE/m	MAE/m	NSE	RMSE/m	MAE/m	NSE
FLSR/Sc1	January	2.679	2.142	−0.353	3.632	3.402	−1.890
	February	2.568	2.083	−0.413	3.528	3.357	−1.841
	March	2.674	2.157	−0.371	3.524	3.301	−1.799
	April	2.703	2.175	−0.384	3.560	3.393	−1.530
	May	3.113	2.484	−0.901	3.650	3.377	−1.450
	June	3.123	2.438	−0.845	3.701	3.523	−1.766
	July	3.130	2.483	−0.841	3.939	3.943	−2.112
	August	3.046	2.426	−0.736	3.917	3.733	−2.042
	September	3.194	2.832	−0.966	3.823	3.775	−2.131
	October	2.891	2.339	−0.522	3.810	3.723	−2.086
	November	2.770	2.250	−0.408	3.665	3.487	−1.409
	December	3.079	2.459	−0.382	3.761	3.587	−1.990
FLSR/Sc2	January	2.224	1.846	0.051	2.127	2.724	−1.489
	February	2.667	2.177	−0.331	2.979	2.836	−1.842
	March	3.507	2.775	−1.394	2.750	2.301	−1.373
	April	2.638	2.237	−0.308	3.013	1.725	−1.610
	May	3.276	2.642	−1.105	2.853	2.625	−1.449
	June	3.126	2.539	−0.848	3.032	2.917	−1.615
	July	2.827	2.258	−0.513	3.204	3.041	−1.954
	August	3.330	2.663	−1.232	2.861	2.799	−1.540
	September	3.024	2.376	−0.633	3.406	3.587	−1.712
	October	3.139	2.546	−0.795	2.924	2.757	−1.647
	November	2.874	2.429	−0.408	2.861	2.799	−1.540
	December	2.741	2.272	−0.416	2.914	2.796	−1.766
FLSR/Sc3	January	2.296	1.229	−0.215	2.343	1.516	−0.322
	February	2.308	1.219	−0.103	2.350	1.372	−0.240
	March	2.340	1.121	0.002	2.609	2.279	−1.028
	April	2.321	1.272	0.007	2.611	1.399	−1.019
	May	3.244	2.115	−1.065	3.770	2.543	−1.244
	June	3.356	2.430	−1.129	3.843	2.716	−1.400
	July	2.554	1.352	−0.226	2.301	1.279	−0.288
	August	2.798	1.566	−0.071	2.415	1.600	−0.139
	September	2.941	1.682	−0.638	2.865	1.503	−1.050
	October	3.094	2.131	−0.926	3.722	2.453	−1.132
	November	3.781	2.109	−1.784	3.921	2.742	−1.521
	December	2.542	1.440	−0.518	2.890	1.600	−1.142

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Table 4. Validation indices for FLSR model performance in monthly GWL estimation under different Scenarios

Model/Sc	Month	Training					Testing
Model/Sc	Month	RMSE (m)	MAE (m)	NSE	KGE	PBIAS (%)	RMSE (m)	MAE (m)	NSE	KGE	PBIAS (%)
FLSR/Sc1	January	2.679	2.142	−0.353	−0.312	9.215	3.632	3.402	−1.890	−1.650	15.32
	February	2.568	2.083	−0.413	−0.372	9.512	3.528	3.357	−1.841	−1.610	15.84
	March	2.674	2.157	−0.371	−0.330	9.318	3.524	3.301	−1.799	−1.572	15.11
	April	2.703	2.175	−0.384	−0.343	9.412	3.560	3.393	−1.530	−1.312	13.95
	May	3.113	2.484	−0.901	−0.861	12.32	3.650	3.377	−1.450	−1.210	14.52
	June	3.123	2.438	−0.845	−0.805	11.89	3.701	3.523	−1.766	−1.532	15.67
	July	3.130	2.483	−0.841	−0.801	11.85	3.939	3.943	−2.112	−1.872	18.24
	August	3.046	2.426	−0.736	−0.696	11.21	3.917	3.733	−2.042	−1.802	17.67
	September	3.194	2.832	−0.966	−0.926	12.85	3.823	3.775	−2.131	−1.891	18.43
	October	2.891	2.339	−0.522	−0.482	10.15	3.810	3.723	−2.086	−1.846	17.97
	November	2.770	2.250	−0.408	−0.367	9.472	3.665	3.487	−1.409	−1.178	13.45
	December	3.079	2.459	−0.382	−0.341	9.392	3.761	3.587	−1.990	−1.750	16.32
FLSR/Sc2	January	2.224	1.846	0.051	0.089	5.123	2.127	2.724	−1.489	−1.212	10.45
	February	2.667	2.177	−0.331	−0.293	8.156	2.979	2.836	−1.842	−1.598	12.67
	March	3.507	2.775	−1.394	−1.354	14.67	2.750	2.301	−1.373	−1.132	10.15
	April	2.638	2.237	−0.308	−0.270	7.956	3.013	1.725	−1.610	−1.372	11.32
	May	3.276	2.642	−1.105	−1.065	12.32	2.853	2.625	−1.449	−1.208	10.86
	June	3.126	2.539	−0.848	−0.808	11.21	3.032	2.917	−1.615	−1.374	11.45
	July	2.827	2.258	−0.513	−0.473	9.856	3.204	3.041	−1.954	−1.712	13.45
	August	3.330	2.663	−1.232	−1.192	13.45	2.861	2.799	−1.540	−1.298	10.67
	September	3.024	2.376	−0.633	−0.593	10.46	3.406	3.587	−1.712	−1.470	12.15
	October	3.139	2.546	−0.795	−0.755	10.85	2.924	2.757	−1.647	−1.405	11.67
	November	2.874	2.429	−0.408	−0.368	9.156	2.861	2.799	−1.540	−1.298	10.67
	December	2.741	2.272	−0.416	−0.376	9.256	2.914	2.796	−1.766	−1.524	12.32
FLSR/Sc3	January	2.296	1.229	−0.215	−0.178	3.215	2.343	1.516	−0.322	−0.280	4.320
	February	2.308	1.219	−0.103	−0.068	2.856	2.350	1.372	−0.240	−0.202	3.972
	March	2.340	1.121	0.002	0.038	2.156	2.609	2.279	−1.028	−0.788	8.456
	April	2.321	1.272	0.007	0.043	2.312	2.611	1.399	−1.019	−0.779	8.320
	May	3.244	2.115	−1.065	−1.025	8.856	3.770	2.543	−1.244	−1.004	9.672
	June	3.356	2.430	−1.129	−1.089	9.320	3.843	2.716	−1.400	−1.160	10.46
	July	2.554	1.352	−0.226	−0.188	3.456	2.301	1.279	−0.288	−0.248	4.120
	August	2.798	1.566	−0.071	−0.035	2.712	2.415	1.600	−0.139	−0.103	3.456
	September	2.941	1.682	−0.638	−0.598	6.156	2.865	1.503	−1.050	−0.810	8.672
	October	3.094	2.131	−0.926	−0.886	7.856	3.722	2.453	−1.132	−0.892	9.320
	November	3.781	2.109	−1.784	−1.744	12.46	3.921	2.742	−1.521	−1.281	10.97
	December	2.542	1.440	−0.518	−0.478	5.856	2.890	1.600	−1.142	−0.902	9.120

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参考文献(38)

Adnan RM, Dai HL, Mostafa RR, et al. 2023. Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data. Geocarto International, 38(1): 2158951. DOI: 10.1080/10106049.2022.2158951.

Ahmadi A, Olyaei M, Heydari Z, et al. 2022. Groundwater level modeling with machine learning: A systematic review and meta-analysis. Water, 14(6): 949. DOI: 10.3390/w14060949.

Ahmadifar R, Safavi HR, Mirabbasi R, et al. 2025. A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty. Environmental Monitoring and Assessment, 197(4): 403. DOI: 10.1007/s10661-025-13856-3.

Ang YK, Talei A, Zahidi I, et al. 2023. Past, present, and future of using neuro-fuzzy systems for hydrological modeling and forecasting. Hydrology, 10(2): 36. DOI: 10.3390/hydrology10020036.

Aryafar A, Khosravi V, Karami, S. 2020. Groundwater quality assessment of Birjand plain aquifer using kriging estimation and sequential Gaussian simulation methods. Environmental Earth Sciences, 79: 1−21. DOI: 10.1007/s12665-020-08905-8.

Asadolahi M, Akbari MG, Hesamian G, et al. 2021. A robust support vector regression with exact predictors and fuzzy responses. International Journal of Approximate Reasoning, 132: 206−225. DOI: 10.1016/j.ijar.2021.02.006.

Bardossy A, Duckstein L. 2022. Fuzzy rule-based modeling with applications to geophysical, biological, and engineering systems. CRC Press, London, United Kingdom. DOI: 10.1201/9780138755133

Bhadani V, Singh A, Kumar, V, et al. 2024. Nature-inspired optimal tuning of input membership functions of a fuzzy inference system for groundwater level prediction. Environmental Modelling and Software, 175: 105995. DOI: 10.1016/j.envsoft.2024.105995.

Bogardi I, Bardossy A, Duckstein L. 1983. Regional management of an aquifer for mining under fuzzy environmental objectives. Water Resources Research, 19(6): 1394−1402. DOI: 10.1029/WR019i006p01394.

Boo KBW, El-Shafie A, Othman F, et al. 2024. Groundwater level forecasting using ensemble coactive neuro-fuzzy inference system. Science of The Total Environment, 912: 168760. DOI: 10.1016/j.scitotenv.2023.168760.

Borzì I. 2025. Modeling groundwater resources in data-scarce regions for sustainable management: Methodologies and limits. Hydrology, 12(1): 2306−5338. DOI: 10.3390/hydrology12010011.

Condon LE, Kollet S, Bierkens MF, et al. 2021. Global groundwater modeling and monitoring: Opportunities and challenges. Water Resources Research, 57(12): e2020WR029500. DOI: 10.1029/2020WR029500.

Feng F, Ghorbani H, Radwan AE. 2024. Predicting groundwater level using traditional and deep machine learning algorithms. Frontiers in Environmental Science, 12: 1291327. DOI: 10.3389/fenvs.2024.1291327.

Hoogesteger J. 2022. Regulating agricultural groundwater use in arid and semi-arid regions of the Global South: Challenges and socio-environmental impacts. Current Opinion in Environmental Science and Health, 100341. DOI: 10.1016/j.coesh.2022.100341

Hu H, Yang K, Sharma A, et al. 2020. Assessment of water and energy scarcity, security and sustainability into the future for the Three Gorges Reservoir using an ensemble of RCMs. Journal of Hydrology, 586: 124893. DOI: 10.1016/j.jhydrol.2020.124893.

Kambalimath S, Deka PC. 2020. A basic review of fuzzy logic applications in hydrology and water resources. Applied Water Science, 10(8): 1−14. DOI: 10.1007/s13201-020-01276-2.

Koonce JE. 2016. Water balance and moisture dynamics of an arid and semi-arid soil: A weighing lysimeter and field study. DOI: 10.34917/9112095

Kuang X, Liu J, Scanlon BR, et al. 2024. The changing nature of groundwater in the global water cycle. Science, 383(6686): eadf0630. DOI: 10.1126/science.adf0630.

Lall U, Josset L, Russo T. 2020. A snapshot of the world's groundwater challenges. Annual Review of Environment and Resources, 45(1): 171−194. DOI: 10.1146/annurev-environ-102017-025800.

Loucks DP. 2023. Hydroinformatics: A review and future outlook. Cambridge Prisms: Water, 1: e10. DOI: 10.1017/wat.2023.10.

Malakar P, Bhanja SN, Dash AA, et al. 2022. Delineating variabilities of groundwater level prediction across the agriculturally intensive transboundary aquifers of South Asia. ACS ES& T Water, 3(6): 1547−1560. DOI: 10.1021/acsestwater. 2c00220.

Moges E, Demissie Y, Larsen L, et al. 2021. Sources of hydrological model uncertainties and advances in their analysis. Water, 13(1): 28. DOI: 10.3390/w13010028.

NavaleV, Mhaske S. 2023. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) model for Forecasting groundwater level in the Pravara River Basin, India. Modeling Earth Systems and Environment, 9(2): 2663−2676. DOI: 10.1007/s40808-022-01639-5.

Rahimi M, Ebrahimi H. 2023. Data-driven driven to underground water level using artificial intelligence hybrid algorithms. Scientific Reports, 13(1): 10359. DOI: 10.1038/s41598-023-35255-9.

Ramezani N. 2022. Modern statistical modeling in machine learning and big data analytics: Statistical models for continuous and categorical variables. In research anthology on machine learning techniques, methods, and applications (pp. 90−106). IGI Global. DOI: 10.4018/978-1-6684-6291-1.ch007.

Rashidi GH, Katibeh H, Maleki A. 2024. Forecasting groundwater fluctuations caused by earthquakes using fuzzy logic and AHP Method: A case study from Iran. Earth Science Informatics, 17(3): 2143−2158. DOI: 10.1007/s12145-024-01264-z.

Saikrishnamacharyulu I, Mohanta NR, Kumar MH, et al. 2022. Simulation of water table depth using a hybrid CANFIS model: A Case study. In Intelligent System Design: Proceedings of INDIA 2022 (pp. 319−328). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-19-4863-3_30.

Samani S, Vadiati M, Azizi F, et al. 2022. Groundwater level simulation using soft computing methods with emphasis on major meteorological components. Water Resources Management, 36(10): 3627−3647. DOI: 10.1007/s11269-022-03217-x.

Samantaray S, Biswakalyani C, Singh DK, et al. 2022. Prediction of groundwater fluctuation based on a hybrid ANFIS-GWO approach in an arid Watershed, India. Soft Computing, 26(11): 5251−5273. DOI: 10.1007/s00500-022-07097-6.

Samantaray S, Sahoo A, Baliarsingh F. 2024. Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm. Cleaner Water, 1: 100003. DOI: 10.1016/j.clwat.2024.100003.

Sun J, Hu L, Li D, et al. 2022. Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. Journal of Hydrology, 608: 127630. ‏ DOI: 10.1016/j.jhydrol.2022.127630

Tao H, Hameed MM, Marhoon HA, et al. 2022. Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing, 489: 271−308. DOI: 10.1016/j.neucom.2022.03.014.

Theodoridou PG, Varouchakis EA, Karatzas GP. 2017. Spatial analysis of groundwater levels using fuzzy logic and geostatistical tools. Journal of Hydrology, 555: 242−252. DOI: 10.1016/j.jhydrol.2017.10.027.

Varouchakis EA, Theodoridou PG, Karatzas GP. 2019. Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background. Journal of Hydrology, 575: 487−498. DOI: 10.1016/j.jhydrol.2019.05.055.

Yang X, Zhang Z. 2022. A CNN-LSTM model based on a meta-learning algorithm to predict groundwater level in the middle and lower reaches of the Heihe River, China. Water, 14(15): 2377. DOI: 10.3390/w14152377.

Zaresefat M, Derakhshani R. 2023. Revolutionizing groundwater management with hybrid AI models: A practical review. Water, 15(9): 1750. DOI: 10.3390/w15091750.

Zarinmehr H, Tizro AT, Fryar AE, et al. 2022. Prediction of groundwater level variations based on gravity recovery and climate experiment (GRACE) satellite data and a time-series analysis: a case study in the Lake Urmia basin, Iran. Environmental Earth Sciences, 81(6): 180. DOI: 10.1007/s12665-022-10296-x.

Zowam FJ, Milewski AM. 2024. Groundwater level prediction using machine learning and geostatistical interpolation models. Water, 16(19): 2771. DOI: 10.3390/w16192771.

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[4]	Sajjad Moradi Nazarpoor, Mohsen Rezaei, Hadi Jafari, Yazdan Mohebi, Reza Mirbageri2025: Introducing a new geostatistical approach to classify groundwater samples based on Stiff diagram: Case study of Chahardoly aquifer, west of Iran, Journal of Groundwater Science and Engineering, 13, 423-433. doi: 10.26599/JGSE.2025.9280063
[5]	ILUNGA Nyembwe, AMADI Akobundu Nwanosike, Gilbert NDATIMANA, Nelson OKOT, Raphaël TSHIMANGA Muamba2024: Evaluation of aquifer hydraulic properties from resistivity and pumping test data in parts of Gwagwalada, Northcentral Nigeria, Journal of Groundwater Science and Engineering, 12, 309-320. doi: 10.26599/JGSE.2024.9280023
[6]	Li-qiang Ge, Yan-pei Cheng, Qing Yi, Xue-ru Wen, Hua Dong, Kun Liu, Jian-kang Zhang2024: Research on groundwater ecological environment mapping based on ecological service function: A case study of five Central Asian countries and neighboring regions of China, Journal of Groundwater Science and Engineering, 12, 339-346. doi: 10.26599/JGSE.2024.9280025
[7]	Jun Zhang, Rong-zhe Hou, Kun Yu, Jia-qiu Dong, Li-he Yin2024: Impact of water table on hierarchically nested groundwater flow system, Journal of Groundwater Science and Engineering, 12, 119-131. doi: 10.26599/JGSE.2024.9280010
[8]	Jun Liu, Yan-pei Cheng, Feng-e Zhang, Xue-ru Wen, Liu Yang2023: Research hotspots and trends of groundwater and ecology studies: Based on a bibliometric approach, Journal of Groundwater Science and Engineering, 11, 20-36. doi: 10.26599/JGSE.2023.9280003
[9]	Niway Wondesen Fikade, Molla Dagnachew Daniel, Lohani Tarun Kumar2022: Holistic approach of GIS based Multi-Criteria Decision Analysis (MCDA) and WetSpass models to evaluate groundwater potential in Gelana watershed of Ethiopia, Journal of Groundwater Science and Engineering, 10, 138-152. doi: 10.19637/j.cnki.2305-7068.2022.02.004
[10]	GUI Chun-lei, WANG Zhen-xing, MA Rong, ZUO Xue-feng2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN, Journal of Groundwater Science and Engineering, 9, 1-11. doi: 10.19637/j.cnki.2305-7068.2021.01.001
[11]	Qaisar Mehmood, Muhammad Arshad, Muhammad Rizwan, Shanawar Hamid, Waqas Mehmood, Muhammad Ansir Muneer, Muhammad Irfan, Lubna Anjum2020: Integration of geoelectric and hydrochemical approaches for delineation of groundwater potential zones in alluvial aquifer, Journal of Groundwater Science and Engineering, 8, 366-380. doi: 10.19637/j.cnki.2305-7068.2020.04.007
[12]	Yacob T Tesfaldet, Avirut Puttiwongrak, Tanwa Arpornthip2020: Spatial and temporal variation of groundwater recharge in shallow aquifer in the Thepkasattri of Phuket, Thailand, Journal of Groundwater Science and Engineering, 8, 10-19. doi: 10.19637/j.cnki.2305-7068.2020.01.002
[13]	Bahrami Mehdi, Khaksar Elmira, Khaksar Elahe2020: Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran), Journal of Groundwater Science and Engineering, 8, 230-243. doi: 10.19637/j.cnki.2305-7068.2020.03.004
[14]	Abdelhakim LAHJOUJ, Abdellah EL HMAIDI, Karima BOUHAFA2020: Spatial and statistical assessment of nitrate contamination in groundwater: Case of Sais Basin, Morocco, Journal of Groundwater Science and Engineering, 8, 143-157. doi: 10.19637/j.cnki.2305-7068.2020.02.006
[15]	A Muthamilselvan, N Rajasekaran, R Suresh2019: Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu, India, Journal of Groundwater Science and Engineering, 7, 264-281. doi: DOI: 10.19637/j.cnki.2305-7068.2019.03.007
[16]	SOSI Benjamin, BARONGO Justus, GETABU Albert, MAOBE Samson2019: Electrical-hydraulic conductivity model for a weathered-fractured aquifer system of Olbanita, Lower Baringo Basin, Kenya Rift, Journal of Groundwater Science and Engineering, 7, 360-372. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.007
[17]	YU Kai-ning, LI Jian, LI Hui, CHEN Kang, LV Bing-xu, ZHAO Long-hui2016: Statistical characteristics of heavy metals content in groundwater and their interrelationships in a certain antimony mine area, Journal of Groundwater Science and Engineering, 4, 284-292.
[18]	Dana Mawlood, Jwan Mustafa2016: Comparison between Neuman (1975) and Jacob (1946) application for analysing pumping test data of unconfined aquifer, Journal of Groundwater Science and Engineering, 4, 165-173.
[19]	LIU Jun-qiu, XIE Xin-min2016: Numerical simulation of groundwater and early warnings from the simulated dynamic evolution trend in the plain area of Shenyang, Liaoning Province (P.R. China), Journal of Groundwater Science and Engineering, 4, 367-376.
[20]	2013: The Study of Statistical Damage Constitutive Models of Rock and Its Parameters Based on Lade-Duncan Criterion, Journal of Groundwater Science and Engineering, 1, 74-79.