Abstract
Water erosion creates adverse impacts on agricultural production, infrastructure, and water quality across the world, especially in hilly areas. Regional-scale water erosion assessment is essential, but existing models could have been more efficient in predicting the suspended sediment load. Further, data scarcity is a common problem in predicting sediment load. Thus, the current study aimed at modeling the suspended sediment yield of a hilly watershed (i.e., Bino watershed, Uttarakhand-India) using machine learning (ML) algorithms for a data-scarce situation. For this purpose, the ML models, viz., adaptive neuro-fuzzy inference system (ANFIS) and fuzzy logic (FL) were developed using data from ten years (2000–2009) only. Further, runoff and suspended sediment concentration (SSC) were obtained as the primary influencing factors. Varying combinations of lagged SSC and runoff data were considered as model inputs. The ANFIS and FL models were compared with the conventional multiple linear regression (MLR) model. Results indicated that the ANFIS model performed better than the FL and MLR models. Thus, it was concluded that the ANFIS model could be used as a benchmark for sediment yield prediction in hilly terrain in data-scarce situations. The research work would help field investigators in selecting the proper tool for estimating suspended sediment yield/load and policymakers to make appropriate decisions to reduce the devastating impact of soil erosion in hilly terrains.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data cannot be made available because of the policy of data providing agency.
References
Abdolmaleki A, Ghasemi JB (2019) Inhibition activity prediction for a dataset of candidates’ drug by combining fuzzy logic with MLR/ANN QSAR models. Chem Biol Drug Des 93:1139–1157. https://doi.org/10.1111/cbdd.13511
Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl Math Model 39:1837–1852. https://doi.org/10.1016/j.apm.2014.10.016
Abiodun OI, Jantan A, Omolara AE et al (2018) State-of-the-art in artificial neural network applications: A survey. Heliyon 4:e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
Abrahart RJ, See LM (2007) Neural network modelling of non-linear hydrological relationships. Hydrol Earth Syst Sci 11:1563–1579. https://doi.org/10.5194/hess-11-1563-2007
Achite M, Elshaboury N, Jehanzaib M et al (2023) Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin. Algeria Water 15:765. https://doi.org/10.3390/w15040765
Akbarian M, Saghafian B, Golian S (2023) Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran. J Hydrol 620:129480. https://doi.org/10.1016/j.jhydrol.2023.129480
Al-Mahasneh M, Aljarrah M, Rababah T, Alu’datt M (2016) Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology. Food Eng Rev 8:351–366. https://doi.org/10.1007/s12393-016-9141-7
Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson Correlation Coefficient. In: Cohen I, Huang Y, Chen J, Benesty J (eds) Noise Reduction in Speech Processing. Springer Topics in Signal Processing, 2nd edn. Springer Berlin Heidelberg, Berlin, Heidelberg, 1–4
Besalatpour A, Hajabbasi MA, Ayoubi S et al (2012) Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil Sci Plant Nutr 58:149–160. https://doi.org/10.1080/00380768.2012.661078
Bharti B, Pandey A, Tripathi SK, Kumar D (2017) Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models. Hydrol Res 48:1489–1507. https://doi.org/10.2166/nh.2017.153
Biber P, Schwaiger F, Poschenrieder W, Pretzsch H (2021) A fuzzy logic-based approach for evaluating forest ecosystem service provision and biodiversity applied to a case study landscape in Southern Germany. Eur J for Res 140:1559–1586. https://doi.org/10.1007/s10342-021-01418-4
Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33. https://doi.org/10.1016/j.compgeo.2011.09.008
Chalov S, Prokopeva K (2022) Sedimentation and Erosion Patterns of the Lena River Anabranching Channel. Water 14:3845. https://doi.org/10.3390/w14233845
Chalov S, Golosov V, Tsyplenkov A et al (2017) A toolbox for sediment budget research in small catchments. Geogr Environ Sustain 10:43–68. https://doi.org/10.24057/2071-9388-2017-10-4-43-68
Chalov SR, Potemkina TG, Pashkina MP, Kasimov NS (2019) Evolution of Suspended Sediment Budget in the Deltas of Lake Baikal Tributaries. Russ Meteorol Hydrol 44:667–673. https://doi.org/10.3103/S1068373919100042
Chalov S, Prokopeva K, Habel M (2021) North to South Variations in the Suspended Sediment Transport Budget within Large Siberian River Deltas Revealed by Remote Sensing Data. Remote Sens 13:4549. https://doi.org/10.3390/rs13224549
Chalov S, Prokopeva K, Magritsky D et al (2023) Climate change impacts on streamflow, sediment load and carbon fluxes in the Lena River delta. Ecol Indic 157:111252. https://doi.org/10.1016/j.ecolind.2023.111252
Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367:52–61. https://doi.org/10.1016/j.jhydrol.2008.12.024
Cohen S, Svoray T, Laronne JB, Alexandrov Y (2008) Fuzzy-based dynamic soil erosion model (FuDSEM): Modelling approach and preliminary evaluation. J Hydrol 356:185–198. https://doi.org/10.1016/j.jhydrol.2008.04.010
Darabi H, Mohamadi S, Karimidastenaei Z et al (2021) Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models. Soft Comput 25:7609–7626. https://doi.org/10.1007/s00500-021-05721-5
Dong H, Li T, Ding R, Sun J (2018) A novel hybrid genetic algorithm with granular information for feature selection and optimization. Appl Soft Comput 65:33–46. https://doi.org/10.1016/j.asoc.2017.12.048
Elbeltagi A, Di Nunno F, Kushwaha NL et al (2022a) River flow rate prediction in the Des Moines watershed (Iowa, USA): a machine learning approach. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-022-02228-9
Elbeltagi A, Raza A, Hu Y et al (2022c) Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration. Appl Water Sci 12:152. https://doi.org/10.1007/s13201-022-01667-7
Elbeltagi A, Al-Mukhtar M, Kushwaha NL et al (2023a) Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment. Appl Water Sci 13:42. https://doi.org/10.1007/s13201-022-01846-6
Elbeltagi A, Pande CB, Kumar M et al (2023b) Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environ Sci Pollut Res 30:43183–43202. https://doi.org/10.1007/s11356-023-25221-3
Elbeltagi A, Srivastava A, Li P, et al. (2023c). Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021. Journal of Environmental Management, 345:118697. https://doi.org/10.1016/j.jenvman.2023.118697
Gleason CJ (2015) Hydraulic geometry of natural rivers. Prog Phys Geogr Earth Environ 39:337–360. https://doi.org/10.1177/0309133314567584
Gomaa E, Zerouali B, Difi S et al (2023) Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil. Heliyon 9:e18819. https://doi.org/10.1016/j.heliyon.2023.e18819
Goyal MK, Burn DH, Ojha CSP (2012) Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada. Theor Appl Climatol 108:519–534. https://doi.org/10.1007/s00704-011-0546-1
Gu J, Liu S, Zhou Z et al (2022) A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China. Water 14:492. https://doi.org/10.3390/w14030492
Guo C, Jin Z, Guo L et al (2020) On the cumulative dam impact in the upper Changjiang River: Streamflow and sediment load changes. CATENA 184:104250. https://doi.org/10.1016/j.catena.2019.104250
Hadi SJ, Abba SI, Sammen SS et al (2019) Non-Linear Input Variable Selection Approach Integrated With Non-Tuned Data Intelligence Model for Streamflow Pattern Simulation. IEEE Access 7:141533–141548. https://doi.org/10.1109/ACCESS.2019.2943515
Haghbin M, Sharafati A, Motta D et al (2021) Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment. Prog Earth Planet Sci 8:4. https://doi.org/10.1186/s40645-020-00400-9
Hartnett M, Nash S (2017) High-resolution flood modeling of urban areas using MSN_Flood. Water Sci Eng 10:175–183. https://doi.org/10.1016/j.wse.2017.10.003
Herath HMVV, Chadalawada J, Babovic V (2021) Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling. Hydrol Earth Syst Sci 25:4373–4401. https://doi.org/10.5194/hess-25-4373-2021
Huang F, Shangguan W, Li Q et al (2023) Beyond prediction: An integrated post-hoc approach to interpret complex model in hydrometeorology. Environ Model Softw 167:105762. https://doi.org/10.1016/j.envsoft.2023.105762
Idrees MB, Jehanzaib M, Kim D, Kim T-W (2021) Comprehensive evaluation of machine learning models for suspended sediment load inflow prediction in a reservoir. Stoch Environ Res Risk Assess 35:1805–1823. https://doi.org/10.1007/s00477-021-01982-6
Jamali AA, Randhir TO, Nosrati J (2018) Site Suitability Analysis for Subsurface Dams Using Boolean and Fuzzy Logic in Arid Watersheds. J Water Resour Plan Manag 144:4018047. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000947
Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Jones MC (1991) The roles of ISE and MISE in density estimation. Stat Probab Lett 12:51–56. https://doi.org/10.1016/0167-7152(91)90163-L
Jones K, Cortinovis A, Mercangoez M, Ferreau HJ (2017) Distributed Model Predictive Control of Centrifugal Compressor Systems. IFAC-PapersOnLine 50:10796–10801. https://doi.org/10.1016/j.ifacol.2017.08.2343
Kambalimath S, Deka PC (2020) A basic review of fuzzy logic applications in hydrology and water resources. Appl Water Sci 10:191. https://doi.org/10.1007/s13201-020-01276-2
Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52:2263–2293. https://doi.org/10.1007/s10462-017-9610-2
Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H (2014) Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Inf Process Agric 1:14–22. https://doi.org/10.1016/j.inpa.2014.04.001
Kim J, Kasabov N (1999) HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Netw 12:1301–1319. https://doi.org/10.1016/S0893-6080(99)00067-2
Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones. Hydrol Sci J 50:null-96. https://doi.org/10.1623/hysj.2005.50.4.683
Kumar A, Kumar P, Singh VK (2019) Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation. Water Resour Manag 33:1217–1231. https://doi.org/10.1007/s11269-018-2178-z
Kumar A, Singh VK, Saran B et al (2022b) Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques. Sustainability 14:2287. https://doi.org/10.3390/su14042287
Kumar D, Singh VK, Abed SA et al (2023) Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms. Appl Water Sci 13:192. https://doi.org/10.1007/s13201-023-02005-1
Kumar A, Kumar Tripathi V, Sachan P, et al (2022a) Sources of ions in the river ecosystem. In: Madhav S, Kanhaiya S, Srivastav A, et al. (eds) Ecological Significance of River Ecosystems. Elsevier 187–202
Kushwaha NL, Bhardwaj A (2017) Remote Sensing and GIS based Morphometric Analysis for Micro-watershed Prioritization in Takarla-Ballowal Watershed. J Agric Eng 54:48–56
Kushwaha NL, Yousuf A (2017) Soil erosion risk mapping of watersheds using RUSLE, remote sensing and GIS: A review. Res J Agric Sci 8:269–277
Kushwaha NL, Bhardwaj A, Verma VK (2016) Hydrologic response of Takarla-Ballowal watershed in Shivalik foot-hills based on morphometric analysis using remote sensing and GIS. J Indian Water Resour Soc 36:17–25
Kushwaha NL, Rajput J, Elbeltagi A et al (2021) Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India. Atmosphere (basel) 12:1654. https://doi.org/10.3390/atmos12121654
Kushwaha N, Elbeltagi A, Mehan S et al (2022) Comparative study on morphometric analysis and RUSLE-based approaches for micro-watershed prioritization using remote sensing and GIS. Arab J Geosci 15:564. https://doi.org/10.1007/s12517-022-09837-2
Lasheen M, Abdel-Salam M (2018) Maximum power point tracking using Hill Climbing and ANFIS techniques for PV applications: A review and a novel hybrid approach. Energy Convers Manag 171:1002–1019. https://doi.org/10.1016/j.enconman.2018.06.003
Le Chau N, Tran NT, Dao T-P (2020) A multi-response optimal design of bistable compliant mechanism using efficient approach of desirability, fuzzy logic, ANFIS and LAPO algorithm. Appl Soft Comput 94:106486. https://doi.org/10.1016/j.asoc.2020.106486
Lee SK, Mogi G, Hui KS (2013) A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices. Renew Sustain Energy Rev 21:347–355. https://doi.org/10.1016/j.rser.2012.12.067
Liu QJ, Zhang HY, Gao KT et al (2019) Time-frequency analysis and simulation of the watershed suspended sediment concentration based on the Hilbert-Huang transform (HHT) and artificial neural network (ANN) methods: A case study in the Loess Plateau of China. CATENA 179:107–118. https://doi.org/10.1016/j.catena.2019.03.042
Liyew CM, Melese HA (2021) Machine learning techniques to predict daily rainfall amount. J Big Data 8:153. https://doi.org/10.1186/s40537-021-00545-4
Lou HH, Huang YL (2000) Fuzzy-logic-based process modeling using limited experimental data. Eng Appl Artif Intell 13:121–135. https://doi.org/10.1016/S0952-1976(99)00057-3
Lyimo NN, Shao Z, Ally AM et al (2020) A Fuzzy Logic-Based Approach for Modelling Uncertainty in Open Geospatial Data on Landfill Suitability Analysis. ISPRS Int J Geo-Information 9:737. https://doi.org/10.3390/ijgi9120737
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
Malik A, Kumar A, Piri J (2017) Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Comput Electron Agric 138:20–28. https://doi.org/10.1016/j.compag.2017.04.005
Malik A, Kumar A, Kisi O, Shiri J (2019) Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling. Environ Sci Pollut Res 26:22670–22687. https://doi.org/10.1007/s11356-019-05553-9
Markuna S, Kumar P, Ali R et al (2023) Application of Innovative Machine Learning Techniques for Long-Term Rainfall Prediction. Pure Appl Geophys 180:335–363. https://doi.org/10.1007/s00024-022-03189-4
McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe Efficiency Index. J Hydrol Eng 11:597–602. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:6(597)
Meshram SG, Singh VP, Kahya E et al (2022) Assessing erosion prone areas in a watershed using interval rough-analytical hierarchy process (IR-AHP) and fuzzy logic (FL). Stoch Environ Res Risk Assess 36:297–312. https://doi.org/10.1007/s00477-021-02134-6
Metternicht G (2001) Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecol Modell 144:163–179. https://doi.org/10.1016/S0304-3800(01)00371-4
Misra D, Oommen T, Agarwal A et al (2009) Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosyst Eng 103:527–535. https://doi.org/10.1016/j.biosystemseng.2009.04.017
Moorthi PVP, Singh AP, Agnivesh P (2018) Regulation of water resources systems using fuzzy logic: a case study of Amaravathi dam. Appl Water Sci 8:132. https://doi.org/10.1007/s13201-018-0777-8
Moradi AM, Dariane AB, Yang G, Block P (2020) Long-range reservoir inflow forecasts using large-scale climate predictors. Int J Climatol 40:5429–5450. https://doi.org/10.1002/joc.6526
Muhammad J, Muhammad BI, Dongkyun K, Tae-Woong K (2021) Comprehensive Evaluation of Machine Learning Techniques for Hydrological Drought Forecasting. J Irrig Drain Eng 147:4021022. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001575
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I — A discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Nimon KF, Oswald FL (2013) Understanding the Results of Multiple Linear Regression. Organ Res Methods 16:650–674. https://doi.org/10.1177/1094428113493929
Nourani V (2014) A Review on Applications of Artificial Intelligence-Based Models to Estimate Suspended Sediment Load. Int J Soft Comput Eng 3:121–127
Obolewski K, Habel M, Chalov S (2021) River sediment quality and quantity: environmental, geochemical and ecological perspectives. Ecohydrol Hydrobiol 21:565–569. https://doi.org/10.1016/j.ecohyd.2021.11.002
Oyounalsoud MS, Abdallah M, Gokhan Yilmaz A et al (2023) A new meteorological drought index based on fuzzy logic: Development and comparative assessment with conventional drought indices. J Hydrol 619:129306. https://doi.org/10.1016/j.jhydrol.2023.129306
Panda KC, Kumar A, Pradhan SN et al (2021) Impact of Soil Moisture Stress on Rice Productivity in Warming Climate over Indian Mid-Indo-Gangetic Plain. Clim Chang Environ Sustain 9:21–31. https://doi.org/10.5958/2320-642X.2021.00003.X
Panda KC, Singh RM, Thakural LN, Sahoo DP (2022) Representative grid location-multivariate adaptive regression spline (RGL-MARS) algorithm for downscaling dry and wet season rainfall. J Hydrol 605:127381. https://doi.org/10.1016/j.jhydrol.2021.127381
Panda KC, Singh RM, Singh VK et al (2023) Impact of climate change induced future rainfall variation on dynamics of arid-humid zone transition in the western province of India. J Environ Manage 325:116646. https://doi.org/10.1016/j.jenvman.2022.116646
Paramaguru PK, Paul JC, Panigrahi B, Panda KC (2022) Assessment of Replenishable Groundwater Resource and Integrated Water Resource Planning for Sustainable Agriculture. In: Rai PK, Mishra VN, Singh P (eds) Geospatial Technology for Landscape and Environmental Management. Advances in Geographical and Environmental Sciences. Springer Nature Singapore, Singapore, 21–47
Pezeshki Z, Mazinani SM (2019) Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif Intell Rev 52:495–525. https://doi.org/10.1007/s10462-018-9630-6
Pietroń J, Jarsjö J, Romanchenko AO, Chalov SR (2015) Model analyses of the contribution of in-channel processes to sediment concentration hysteresis loops. J Hydrol 527:576–589. https://doi.org/10.1016/j.jhydrol.2015.05.009
Pizzuto J (2020) Suspended sediment and contaminant routing with alluvial storage: New theory and applications. Geomorphology 352:106983. https://doi.org/10.1016/j.geomorph.2019.106983
Preacher KJ, Curran PJ, Bauer DJ (2006) Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis. J Educ Behav Stat 31:437–448. https://doi.org/10.3102/10769986031004437
Rajput J, Kothari M, Bhakar SR (2017) Performance Evaluation of Water Delivery System for Command Area of Left Main Canal of Bhimsagar Irrigation Project, Rajasthan. J Agric Eng 54:57–66
Ren S, Zhang B, Wang W-J et al (2021) Sedimentation and its response to management strategies of the Three Gorges Reservoir, Yangtze River. China CATENA 199:105096. https://doi.org/10.1016/j.catena.2020.105096
Saha S, Gayen A, Pourghasemi HR, Tiefenbacher JP (2019) Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district. India Environ Earth Sci 78:649. https://doi.org/10.1007/s12665-019-8658-5
Sahoo SP, Panda KC (2020) Prediction of Climate Change Using Statistical Downscaling Techniques. In: Rakshit A, Singh HB, Singh AK, et al. (eds) New Frontiers in Stress Management for Durable Agriculture. Springer Singapore, Singapore, 311–328
Sahraei A, Chamorro A, Kraft P, Breuer L (2021) Application of Machine Learning Models to Predict Maximum Event Water Fractions in Streamflow. Front Water 3:652100
Samantaray S, Ghose DK (2019) Sediment assessment for a watershed in arid region via neural networks. Sādhanā 44:219. https://doi.org/10.1007/s12046-019-1199-5
Samantaray S, Biswakalyani C, Singh DK et al (2022) Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India. Soft Comput 26:5251–5273. https://doi.org/10.1007/s00500-022-07097-6
Sarker IH (2021) Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci 2:160. https://doi.org/10.1007/s42979-021-00592-x
Saroughi M, Mirzania E, Vishwakarma DK et al (2023) A Novel Hybrid Algorithms for Groundwater Level Prediction. Iran J Sci Technol Trans Civ Eng. https://doi.org/10.1007/s40996-023-01068-z
Sharghi E, Nourani V, Najafi H, Gokcekus H (2019) Conjunction of a newly proposed emotional ANN (EANN) and wavelet transform for suspended sediment load modeling. Water Supply 19:1726–1734. https://doi.org/10.2166/ws.2019.044
Shukla R, Kumar P, Vishwakarma DK et al (2021) Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques. Theor Appl Climatol. https://doi.org/10.1007/s00704-021-03863-y
Singh H, Gupta MM, Meitzler T et al (2013) Real-Life Applications of Fuzzy Logic. Adv Fuzzy Syst 2013:581879. https://doi.org/10.1155/2013/581879
Singh AK, Kumar P, Ali R et al (2022a) An Integrated Statistical-Machine Learning Approach for Runoff Prediction. Sustainability 14:8209. https://doi.org/10.3390/su14138209
Singh VK, Panda KC, Sagar A et al (2022b) Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity. Eng Appl Comput Fluid Mech 16:1082–1099. https://doi.org/10.1080/19942060.2022.2071994
Sudhishri S, Kumar A, Singh JK et al (2014) Erosion tolerance index under different land use units for sustainable resource conservation in a Himalayan watershed using remote sensing and geographic information system (GIS). African J Agric Res 9:3098–3110. https://doi.org/10.5897/AJAR2013.7933
Tahmoures M, Moghaddam NAR, Naghiloo M (2015) Modeling of streamflow-suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran). DESERT (BIABAN) 20:177–195
Tao H, Al-Khafaji ZS, Qi C et al (2021) Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions. Eng Appl Comput Fluid Mech 15:1585–1612. https://doi.org/10.1080/19942060.2021.1984992
Tarasov MK, Shinkareva GL, Chalov SR, Tutubalina OV (2021) Modeling of the Suspended Matter Balance in the Selenga River Delta Using Remote Sensing Data. Geogr Nat Resour 42:266–275. https://doi.org/10.1134/S1875372821030124
Tayfur G, Brocca L (2015) Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture. Water Resour Manag 29:3519–3533. https://doi.org/10.1007/s11269-015-1012-0
Tayfur G, Ozdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26:1249–1256. https://doi.org/10.1016/j.advwatres.2003.08.005
Tsoukalas VD (2011) An adaptive neuro-fuzzy inference system (ANFIS) model for high pressure die casting. Proc Inst Mech Eng Part B J Eng Manuf 225:2276–2286. https://doi.org/10.1177/0954405411406054
Tsyplenkov A, Vanmaercke M, Golosov V, Chalov S (2020) Suspended sediment budget and intra-event sediment dynamics of a small glaciated mountainous catchment in the Northern Caucasus. J Soils Sediments 20:3266–3281. https://doi.org/10.1007/s11368-020-02633-z
Turhan E, Değerli S (2022) A comparative study of probability distribution models for flood discharge estimation. Geofizika 39:243–257. https://doi.org/10.15233/gfz.2022.39.14
Vatanchi SM, Etemadfard H, Maghrebi MF, Shad R (2023) A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM. Water Resour Manag 37:4769–4785. https://doi.org/10.1007/s11269-023-03579-w
Venkatesh K, Bind YK (2022) ANN and Neuro-Fuzzy Modeling for Shear Strength Characterization of Soils. Proc Natl Acad Sci India Sect A Phys Sci 92:243–249. https://doi.org/10.1007/s40010-020-00709-6
Vesović MV, Jovanović RZ (2022) Adaptive neuro fuzzy Inference systems in identification, modeling and control: The state-of-the-art. Tehnika 77:439–446. https://doi.org/10.5937/tehnika2204439V
Vishwakarma DK, Kumar R, Pandey K et al (2018) Modeling of Rainfall and Ground Water Fluctuation of Gonda District Uttar Pradesh, India. Int J Curr Microbiol Appl Sci 7:2613–2618. https://doi.org/10.20546/ijcmas.2018.705.302
Vishwakarma DK, Ali R, Bhat SA et al (2022a) Pre- and post-dam river water temperature alteration prediction using advanced machine learning models. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-21596-x
Vishwakarma DK, Pandey K, Kaur A et al (2022b) Methods to estimate evapotranspiration in humid and subtropical climate conditions. Agric Water Manag 261:107378. https://doi.org/10.1016/j.agwat.2021.107378
Vishwakarma DK, Kuriqi A, Abed SA et al (2023) Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test. Heliyon 9:e16290. https://doi.org/10.1016/j.heliyon.2023.e16290
Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82
Zadeh LA (1965) Fuzzy sets. Inf. Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zadeh LA (2015) Fuzzy logic—a personal perspective. Fuzzy Sets Syst 281:4–20. https://doi.org/10.1016/j.fss.2015.05.009
Acknowledgements
The authors are grateful to the Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India, and the Divisional Officer, Forest Soil Conservation Department, Ranikhet, Uttarakhand, India, for providing data for this research. The authors are also grateful to the Editors and Anonymous Reviewers for their helpful and constructive comments on an earlier draft of this paper. Alban Kuriqi acknowledges the Portuguese Foundation for Science and Technology (FCT) support through PTDC/CTA-OHR/30561/2017 (WinTherface).
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
Paramjeet Singh Tulla: conceived the problem, conceptualization, methodology, and data collection, and designed the analysis writing (original draft preparation). Dinesh Kumar Vishwakarma: contributed data and formal analysis tools and writing review and editing. Pravendra Kumar: supervision. Preparing maps and writing review and editing: Aman Srivastava, Nand Lal Kushwaha, Jitendra Rajput. Formal analysis, Review, Revising and Editing: Rohitashw Kumar, Alban Kuriqi, Aman Srivastava, Quoc Bao Pham, Kanhu Charan Panda and Ozgur Kisi.
Corresponding authors
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
The authors express their consent to participate in the research and review.
Consent for publication
The authors express their consent for the publication of the research work.
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
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
Tulla, P.S., Kumar, P., Vishwakarma, D.K. et al. Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand. Theor Appl Climatol 155, 4023–4047 (2024). https://doi.org/10.1007/s00704-024-04862-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-024-04862-5