Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms. Journal of Hydrologic Engineering 15(10):729-743
Affandia A K, Watanabe K, Tirtomihardjo H (2007) Application of an artificial neural network to estimate groundwater level fluctuation. Journal of Spatial Hydrology 7(2(
Akhtar M, Ahmad N, Booij M J (2008) The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. Journal of Hydrology 355(1-4):148-163
Alsumaiei A A (2020) A nonlinear autoregressive modeling approach for forecasting groundwater level fluctuation in urban aquifers. Water 12(3):820
Amirahmadi A, Moali Ahari N, Ahmadi T (2013) Determining possible subsidence areas of Ardabil plain using GIS. In Persian
Amisigo B A, van de Giesen N, Rogers C, Andah W E I, Friesen J (2008) Monthly streamflow prediction in the Volta Basin of West Africa: A SISO NARMAX polynomial modelling. Physics and Chemistry of the Earth, Parts A/B/C 33(1):141-150
Azad A, Farzin S, Kashi H, Sanikhani H, Karami H, Kisi O (2018) Prediction of river flow using hybrid neuro-fuzzy models. Arabian Journal of Geosciences 11(22):718
Balamurugan R, Natarajan A M, Premalatha K (2015) Stellar-mass black hole optimization for Biclustering Microarray Gene Expression Data. Applied Artificial Intelligence 29(4):353-381
Basheer I A, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43(1):3-31
Bastola S, François D (2012) Temporal extension of meteorological records for hydrological modelling of Lake Chad Basin (Africa) using satellite rainfall data and reanalysis datasets. Meteorological Applications 19(1):54-70
Bastola S, Misra V (2014) Evaluation of dynamically downscaled reanalysis precipitation data for hydrological application. Hydrological Processes 28(4):1989-2002
Bianchi L, Dorigo M, Gambardella L M, Gutjahr W J (2009) A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing 8:239-287
Bingeman A K, Kouwen N, Soulis E D (2006) Validation of the hydrological processes in a hydrological model. Journal of Hydrologic Engineering 11(5):451-463
Casella G (2002) Statistical inference. Second ed., Duxbury/Thomson Learning, Pacific Grove, CA, 556 p
Chen X Y, Chau K W, Busari A O (2015) A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model. Engineering Applications of Artificial Intelligence 46:258-268
Dehghani R, Poudeh H T (2021) Applying hybrid artificial algorithms to the estimation of river flow: A case study of Karkheh catchment area. Arabian Journal of Geosciences 14(9):768 (In Persian(
Deneubourg J-L, Aron S, Goss S, Pasteels J M (1987) Error, communication and learning in ant societies. European Journal of Operational Research 30(2):168-172
Draper N R, Smith H (1998) Applied regression analysis (Vol. 326). John Wiley & Sons,
Fabio D N, Abba S, Pham B Q, Towfiqul Islam A R M, Talukdar S, Francesco G (2022) Groundwater level forecasting in Northern Bangladesh using nonlinear autoregressive exogenous (NARX) and extreme learning machine (ELM) neural networks. Arabian Journal of Geosciences 15(7):647
Fraser A (1957a) Simulation of genetic systems by automatic digital computers I. Introduction. Australian Journal of Biological Sciences 10(4):484-491
Fraser A (1957b) Simulation of genetic systems by automatic digital computers I, Effects of Linkage on Rates of Advance Under Selection. Australian Journal of Biological Sciences 10(4):492-500
Gaur S, Ch S, Graillot D, Chahar B R, Kumar D N (2013) Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resources Management 27(3):927-941
Glantz S A, Slinker B K, Neilands T B (1990) Primer of applied regression and analysis of variance. Mc Graw Hill. Inc., New York
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment 202:18-27
Goswami M, O’connor K, Bhattarai K (2007) Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment. Journal of Hydrology 333(2-4):517-531
Guan X, Zhang J, Yang Q, Tang X, Liu C, Jin J, Wang G (2020) Evaluation of precipitation products by using multiple hydrological models over the upper Yellow River Basin, China. Remote Sensing 12(24):4023
Guang-Bin H (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks 14(2):274-281
He S, Gu L, Tian J, Deng L, Yin J, Liao Z, Hui Y (2021) Machine learning improvement of streamflow simulation by utilizing remote sensing data and potential application in guiding reservoir operation. Sustainability 13(7):3645
Hernández-Bedolla J, García-Romero L, Franco-Navarro C D, Sánchez-Quispe S T, Domínguez-Sánchez C (2023) Extreme runoff estimation for ungauged watersheds using a new multisite multivariate stochastic model MASVC. Water 15(16):2994
Holland J H (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, p
Huang Q, Qin G, Zhang Y, Tang Q, Liu C, Xia J, Post D (2020) Using remote sensing data‐based hydrological model calibrations for predicting runoff in ungauged or poorly gauged catchments. Water Resources Research 56(8(
Hydrogeol J (2010) The relation between karst spring discharge and rainfall by cross-correlation analysis (Campania, southern Italy). Hydrogeology Journal 18:1881-1895
Hydrology A N N (2000) Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering 5(2):115-123
Hyndman R J, Koehler A B (2006) Another look at measures of forecast accuracy. International Journal of Forecasting 22(4):679-688
Jemcov I, Petric M (2009) Measured precipitation vs. effective infiltration and their influence on the assessment of karst systems based on results of the time series analysis. Journal of Hydrology 379(3):304-314
Joo C, Koo J, Yu M (2002) Application of short-term water demand prediction model to Seoul. Water Science and Technology 46(6-7):255-261
Kalra A, Aryal A, Bhusal A, Gupta R (2023) Floodplain mapping of ungauged watershed using HEC models and PERSIANN precipitation. Paper Presented at the World Environmental and Water Resources Congress
Kanishka G, Eldho T (2020) Streamflow estimation in ungauged basins using watershed classification and regionalization techniques. Journal of Earth System Science 129:1-18
Khaki M, Yusoff I, Islami N (2015) Simulation of groundwater level through artificial intelligence system. Environmental Earth Sciences 73:8357-8367
Khalili K, Abbaszade afshar m, Nazeri Z (2014) Compared to the normal mechanism becomes the normal monthly rainfall data from different regions of Iran. Water and Soil 28(2):365-372 (In Persian(
Kisi O, Alizamir M, Zounemat-Kermani M (2017) Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards 87(1):367-381
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2(2):164-168
Li H, Lu Y, Zheng C, Yang M, Li S (2019) Groundwater level prediction for the arid oasis of Northwest China based on the artificial bee colony algorithm and a back-propagation neural network with double hidden layers. Water 11(4):860
Li K, Coe M, Ramankutty N, De Jong R (2007) Modeling the hydrological impact of land-use change in West Africa. Journal of Hydrology 337(3-4):258-268
Lin T-N, Giles C L, Horne B G, Kung S-Y (1997) A delay damage model selection algorithm for NARX neural networks. IEEE Transactions on Signal Processing 45(11):2719-2730
Maswood M, Hossain F (2016) Advancing river modelling in ungauged basins using satellite remote sensing: The case of the Ganges–Brahmaputra–Meghna basin. International Journal of River Basin Management 14(1):103-117
MATLAB V (2013) 8.2. 0.701. The MathWorks Inc 2013
Meresa H (2019) Modelling of river flow in ungauged catchment using remote sensing data: Application of the empirical (SCS-CN), artificial neural network (ANN) and hydrological model (HEC-HMS). Modeling Earth Systems and Environment 5:257-273
Merz R, Blöschl G (2004) Regionalisation of catchment model parameters. Journal of Hydrology 287(1-4):95-123
Mirarabi A, Nassery H, Nakhaei M, Adamowski J, Akbarzadeh A, Alijani F (2019) Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environmental Earth Sciences 78(15):1-15
Mishra P, Babu R R (2022) Remote Sensing and Geographic Information System (GIS) based runoff estimation from an agricultural watershed. Journal of Soil and Water Conservation 21(2):167-175
Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Thépaut J N (2021) ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13(9):4349-4383
Muñoz Sabater J (2019) ERA5-land monthly averaged data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Earth System Science Data 55:5679-5695
Nash J E, Sutcliffe J V (1970) River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology 10(3):282-290
Nogueira Filho F J M, Souza Filho F d A, Porto V C, Vieira Rocha R, Sousa Estácio Á B, Martins E S P R (2022) Deep learning for streamflow regionalization for ungauged basins: Application of long-short-term-memory cells in semiarid regions. Water 14(9):1318
Noor H M, Ndzi D, Yang G, Safar N Z M (2017). Rainfall-based river flow prediction using NARX in Malaysia. Paper presented at the 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA(
Oudin L, Andréassian V, Perrin C, Michel C, Le Moine N (2008) Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments. Water Resources Research 44(3(
Padilla A, Pulido-Bosch A (1995) Study of hydrographs of karstic aquifers by means of correlation and cross-spectral analysis. Journal of Hydrology 168(1):73-89
Panagopoulos G, Lambrakis N (2006) The contribution of time series analysis to the study of the hydrodynamic characteristics of the karst systems: Application on two typical karst aquifers of Greece (Trifilia, Almyros Crete). Journal of Hydrology 329(3):368-376
Pasteels J M, Deneubourg J-L, Goss S (1987) Self-organization mechanisms in ant societies. I: Trail recruitment to newly discovered food sources. Experientia: Supplementum (54):155-175
Post D A (2009) Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics. Hydrology Research 40(5):433-444
Rezaei M J, Rezaei M R (2020) The estimation of groundwater level changes using four different techniques of evolutionary neural network, case study of Dasht-e-Abbas plain, Ilam province. Watershed Engineering and Management 12(3):737-755 (In Persian(
Rostamzadeh H, Asadi E, Jararzadeh J (2015) Evaluation of the groundwater table using multi-criteria decision making and spatial analysis, case study: Ardebil Plain. Journal of Spatial Analysis Environmental Hazards 2(1):31-42 (In Persian(
Shao Y, Zhao J, Xu J, Fu A, Li M (2022) Application of rainfall-runoff simulation based on the NARX Dynamic neural network model. Water 14(13):2082
Shen Y, Liu D, Jiang L, Yin J, Nielsen K, Bauer-Gottwein P, Wang J (2020) On the contribution of satellite altimetry-derived water surface elevation to hydrodynamic model calibration in the han river. Remote Sensing 12(24):4087
Sobhani B, Nasiri f (2022) Agro-ecological zonation of canola cultivation in Ardebil Plain. Journal of Applied Researches in Geographical Sciences 22(65):61-78 (In Persian(
Tarpanelli A, Santi E, Tourian M J, Filippucci P, Amarnath G, Brocca L (2018) Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network. IEEE Transactions on Geoscience and Remote Sensing 57(1):329-341
Thapa S, Zhao Z, Li B, Lu L, Fu D, Shi X, Qi H (2020) Snowmelt-driven streamflow prediction using machine learning techniques (LSTM, NARX, GPR, and SVR). Water 12(6):1734
Tiwari D K, Tiwari H, Nateriya R (2022) Geomorphology-wavelet based approach to rainfall runoff modeling for data scarce semi-arid regions, Kolar river catchment, India. Journal of Engineering Research 10(1B):29-40
Toksarı M D (2007) Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35(8):3984-3990
Trenberth K E, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of the global water budget and its annual cycle using observational and model data. Journal of Hydrometeorology 8(4):758-769
Trichakis I C, Nikolos I K, Karatzas G P (2011) Artificial Neural Network (ANN) based modeling for karstic groundwater level simulation. Water Resources Management 25(4):1143-1152
Wagener T, Wheater H, Gupta H V (2004) Rainfall-runoff modelling in gauged and ungauged catchments. World Scientific, 4-8 p
Xu C-Y (1999) Estimation of parameters of a conceptual water balance model for ungauged catchments. Water Resources Management 13:353-368
Xue H, Liu J, Dong G, Zhang C, Jia D (2022) Runoff estimation in the upper reaches of the heihe river using an LSTM model with remote sensing data. Remote Sensing 14(10):2488
Young A R (2006) Stream flow simulation within UK ungauged catchments using a daily rainfall-runoff model. Journal of Hydrology 320(1-2):155-172
Yu H, Wilamowski B M (2011) Levenberg-marquardt training. Industrial Electronics Handbook 5(12):1
Yu L, Liu K, Li K (2007) Ant Colony Optimization in continuous problem. Frontiers of Mechanical Engineering in China 2(4):459-462
)