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Earth Science Informatics

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24.01.2024 | RESEARCH

Prediction of reservoir evaporation considering water temperature and using ANFIS hybridized with metaheuristic algorithms

verfasst von: Boudjerda Marouane, Mohammed Abdullahi Mu’azu, Andrea Petroselli‬

Erschienen in: Earth Science Informatics | Ausgabe 2/2024

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Abstract

Accurately estimating evaporation is essential for water managers to formulate effective rules and policies. The complexity arising from intricate interactions within the soil-atmosphere system makes evaporation a challenging parameter to predict. In the past decade, Machine Learning techniques rooted in soft computing have emerged as potent tools for addressing the intricacies and non-linearities in hydrology. Surprisingly, there has been no prior research exploring the impact of reservoir water temperature on evaporation modeling. Consequently, this study aimed to employ a hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA). The focus was on modeling the monthly evaporation of the Boukourdane Dam in Algeria for the period of September 1996 to August 2016 and examining how the reservoir's water temperature influences model performance based on four performance indicators, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), scatter index (SI) and Correlation Coefficient (R). The findings underscored the significance of incorporating the water temperature parameter. Among the models, ANFIS-HHO demonstrated the highest accuracy with MAE, RMSE and R values of 1.28, 1.21 mm and 0.92, respectively during test period. Moreover, results revealing a notable impact of reservoir water temperature on evaporation forecasting and the addition of this parameter provide an increase in R and decrease in RMSE about 4.54% and 17.98% respectively.

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Abed M, Imteaz MA, Ahmed AN, Huang YF (2021) Application of long short-term memory neural network technique for predicting monthly pan Evaporation. Sci Rep 11:20742. https://doi.org/10.1038/s41598-021-99999-yADSCrossRefPubMedPubMedCentral

Adnan RM, Mostafa RR, Elbeltagi A, Yaseen ZM, Shahid S, Kisi O (2022a) Development of new machine learning model for streamflow prediction: case studies in Pakistan. Stoch Env Res Risk Assess 36:999–1033. https://doi.org/10.1007/s00477-021-02111-z(0123456789)CrossRef

Adnan RM, Jaafari A, Milan SG, Kisi O, Heddam S, Zounemat-Kermani M (2022b) Hybridized Adaptive Neuro-Fuzzy Inference System with Metaheuristic Algorithms for Modeling Monthly Pan Evaporation. Water 14:3549. https://doi.org/10.3390/w14213549CrossRef

Adnan RM, Petroselli A, Heddam S, Guimarães Santos CA, Kisi O (2021a) Comparison of different methodologies for rainfall-runoff modeling: machine learning vs conceptual approach. Nat Hazards 105:2987–3011. https://doi.org/10.1007/s11069-020-04438-2CrossRef

Adnan RM, Petroselli A, Heddam S, Guimarães Santos CA, Kisi O (2021b) Short term rainfall-runoff modelingusing several machine learning methods and a conceptual event-based model. Stoch Env Res Risk A 35:597–616. https://doi.org/10.1007/s00477-020-01910-0CrossRef

Ahmadi F, Mehdizadeh S, Mohammadi B, Pham QB, Doan TN, Vo ND (2021) Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agric Water Manag 244:106622. https://doi.org/10.1016/j.agwat.2020.106622CrossRef

Al Domany M (2017) L’évaporation dans le bilan hydrologique des étangs du Centre-Ouest de la France (Brenne et Limousin). Thèse de doctorat, Université d’Orléans, p 332

Alizamir M, Kisi O, Adnan RM, Kuriqi A (2020) Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophysica 68(3):14. https://doi.org/10.1007/s11600-020-00446-9CrossRef

Allawi MF, Othman FB, Afan HA, Ahmed AN, Hossain MS, Fai CM, El-Shafie A (2019) Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods. Water 11:1226. https://doi.org/10.3390/w11061226CrossRef

Althof D, Rodrigues LN, Silva DD (2019) Evaluating Evaporation Methods for Estimating Small Reservoir Water Surface Evaporation in the Brazilian Savannah. Water 11:1942. https://doi.org/10.3390/w11091942CrossRef

Azad A, Manoochehri M, Kashi H, Farzin S, Karami H, Shiri J (2019) Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling. J Hydrol 571:214–224. https://doi.org/10.1016/j.jhydrol.2019.01.062CrossRef

Azar A, Milan NG, Kayhomayoon Z (2021) Predicting monthly evaporation from dam reservoirs using LS-SVR and ANFIS optimized by Harris hawks optimization algorithm. Environ Monit Assess 193:695. https://doi.org/10.1007/s10661-021-09495-zCrossRef

Bui T, Khosravi K, Li S, Shahabi H, Panahi M, Singh VP, Chapi K, Shirzadi A, Panahi S, Chen W et al (2018) New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling. Water 10:1210. https://doi.org/10.3390/w10091210CrossRef

Calp MH (2019) A Hybrid ANFIS-GA approach for estimation of regional rainfall amount. GU J Sci 32(1):145–162

Cappelli F, Tauro F, Apollonio C, Petroselli A, Borgonovo E, Grimaldi S (2023) Feature importance measures to dissect the role of sub-basins in shaping the catchment hydrological response: a proof of concept. Stoch Env Res Risk A 37(4):1247–1264. https://doi.org/10.1007/s00477-022-02332-wCrossRef

Chaudhury S, Samantaray S, Sahoo A, Bhagat B, Biswakalyani C, Satapathy DP (2022) Hybrid ANFIS-PSO Model for Monthly Precipitation Forecasting. In: Bhateja V, Tang J, Satapathy SC, Peer P, Das R (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, 267th edn. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_33CrossRef

Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena 157:310–324. https://doi.org/10.1016/j.catena.2017.05.034CrossRef

Cosanday C, Robinson M (2012) Hydrologie continentale. Armand Colin, ParisCrossRef

Dehghani M, Akram Seifib A, Hossien Riahi-Madvar H (2019) flow prediction by combining ANFIS and grey wolf optimization. Journal of Hydrology 576:698–725. https://doi.org/10.1016/j.jhydrol.2019.06.065ADSCrossRef

Deo RC, Samui P, Kim D (2016) Estimation of monthly evaporative loss using relevance vector machine, extreme learningmachine andmultivariate adaptive regression spline models. Stoch Env Res Risk Assess 30(6):1769–1784. https://doi.org/10.1007/s00477-015-1153-yCrossRef

Dong L, Zeng W, Wu L, Lei G, Chen H, Srivastava AK, Gaiser T (2021) Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm. Water 13:256. https://doi.org/10.3390/w13030256CrossRef

Duan Z, Bastiaanssen WGM (2017) Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model. Environ Res Lett 12:024005. https://doi.org/10.1088/1748-9326/aa568eADSCrossRef

Eray O, Mert C, Kisi O (2018) Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation. Hydrol Res 49(4):1221–1233. https://doi.org/10.2166/nh.2017.076. nh2017076CrossRef

Eshetu KD, Alamirew T, Ayele T, Woldesenbet D (2023) Interpretable machine learning for predicting evaporation from Awash reservoirs. Earth Science Informatics, Ethiopia. https://doi.org/10.1007/s12145-023-01063-yCrossRef

Feng W, Lu H, Yao T, Yu Q (2020) Drought characteristics and its elevation dependence in the Qinghai-Tibet plateau during the last half-century. Sci Rep 10:14323. https://doi.org/10.1038/s41598-020-71295-1ADSCrossRefPubMedPubMedCentral

Friedrich K, Grossman RL, Huntington J, Blanken PD, Lenters J, Holman KD, Gochis D, Livneh B, ErikSkeie PJ, Healey NC, Dahm K, Pearson C, Finnessey T, Hook SJ, Kowalski T (2018) Reservoir evaporation in the Western United States: current science, challenges, and future needs. Bull Am Meteorol Soc.https://doi.org/10.1175/BAMS-D-15-00224.1

Ghorbani MA, Deo RC, Yaseen ZM, Kashani MH, Mohammadi B (2018) Pan evaporation prediction using a hybridmultilayer perceptron-firefly algorithm (MLP-FFA) model: Case study in North Iran. Theor Appl Climatol 133(3):1119–1131. https://doi.org/10.1007/s00704-017-2244-0ADSCrossRef

Ghose DK, Sahoo A, Tanaya K, Kumar U (2022) Performance Evaluation of hybrid ANFIS model for Flood Prediction. In: 8th International Conference on Advanced Computing and Communication Systems (ICACCS), pp 772–777. https://doi.org/10.1109/ICACCS54159.2022.9785002

Haznedar B, Kilinc HC (2022) A Hybrid ANFIS-GA approach for estimation of hydrological time series. Water Resour Manag 36:4819–4842. https://doi.org/10.1007/s11269-022-03280-4CrossRef

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm andapplications. Future Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028CrossRef

Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. University Michigan Press, Ann Arbor

Hundecha Y, Bardossy A, Werner H (2001) Development of a fuzzy logic-based rainfall-runoff model. Hydrol Sci J 46(3):363–376. https://doi.org/10.1080/02626660109492832CrossRef

Jang JSR (1993) Anfis: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRef

Jasmine M, Mohammadian A, Bonakdari H (2022) On the prediction of evaporation in arid climate using machine learning model. Math Comput Appl 27:32. https://doi.org/10.3390/mca27020032

Kayhomayoon Z, Naghizadeh F, Malekpoor M, Arya-Azar N, Ball J, Ghordoyee-Milan S (2022) Prediction of evaporation from dam reservoirs under climate change using soft computing techniques. Environ Sci Pollut Res 30:27912–27935. https://doi.org/10.1007/s11356-022-23899-5CrossRef

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, 1942–1945

Keshtegar B, Kisi O, Ghohani H, Zounemat-Kermani M (2018) Subset modeling basis ANFIS for prediction of the reference evapotranspiration”. Water Resour Manage 32(3):1101–1116. https://doi.org/10.1007/s11269-017-1857-5CrossRef

Keskin ME, Terzi O (2006) Evaporation estimation models for Lake E˘girdir, Turkey. Hydrol Process 20:2381–2391. https://doi.org/10.1002/hyp.6049ADSCrossRef

Khosravia K, Daggupatia P, Alamib MT, Awadhc SM, Gharebd MI, Panahii M, Phamf BT, Rezaiee F, Qig C, Yaseen ZM (2019) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Comput Electron Agric 167:105041. https://doi.org/10.1016/j.compag.2019.105041CrossRef

Kisi O, Ozturk O (2007) Adaptive neuro-fuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133(4):368–379. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:4(368)CrossRef

Kisi O, Sanikhani H, Zounemat-Kermani M (2014) Comparison of two different adaptive Neuro-Fuzzy inference system in modelling daily reference evapotranspiration. Water Resour Manag 28:2655–2675. https://doi.org/10.1007/s11269-014-0632-0CrossRef

Knapp HV, Yu YS, Pogge EC (1984) Monthlyevaporation for Milford Lake in Kansas". J Irrig Drain Eng 110(2):138–148CrossRef

Li MF, Tang XP, Wu W, Liu HB (2013) General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Convers Manag 70:139–148CrossRef

Malik A, Kumar A, Kisi O (2017) Monthly pan evaporation estimation in Indian central Himalayas using different heuristic approaches and climate based models. Comput Electron Agric 143:302–313. https://doi.org/10.1016/j.compag.2017.11.008CrossRef

Malik A, Kumar A, Kim S, Kashani MH, Karimi V, Sharafati A, Ghorbani MA, Al-Ansari N, Salih SQ, Yaseen ZM, Chau K-W (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: Application ofmultiple learning artificial intelligence model. Eng Applic Comput Fluid Mech 14(1):323–338. https://doi.org/10.1080/19942060.2020.1715845CrossRef

Maroufpoor E, Sanikhani H, Emamgholizadeh S, Kişi Ö (2018) Estimation of wind drift and evaporation losses from sprinkler irrigation systems by different data-driven methods. Irrig Drain 67(2):222–232CrossRef

Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002CrossRef

Moayedi M, Ghareh S, Foong LK (2021) Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling. Eng Comput 38:1331–1347. https://doi.org/10.1007/s00366-020-01277-4CrossRef

Moazenzadeh R, Mohammadi B, Shamshirband S, Chau KW (2018) Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Eng Applic Comput Fluid Mech 12(1):584–597. https://doi.org/10.1080/19942060.2018.1482476CrossRef

Mohammadi B (2023) Modeling various drought time scales via a merged artificial neural network with a firefly algorithm. Hydrology 10(3):58. https://doi.org/10.3390/hydrology10030058CrossRef

Mohamadi S, Ehteram M, El-Shafie A (2020) Accuracy enhancement formonthly evaporation predictingmodel utilizing evolutionary machine learningmethods. Int J Environ Sci Technol 17(7):3373–3396. https://doi.org/10.1007/s13762-019-02619-6CrossRef

Moghaddas F, Kabiri-Samani A, Zekri M, Azamathulla HM (2021) Combined APSO-ANN and APSO-ANFIS models for prediction of pressure loss in air-water two-phase slug flow in a horizontal pipeline. J Hydroinform 23:1. https://doi.org/10.2166/hydro.2020.300CrossRef

Mosavi A, Ozturk P, Chau K (2018) Flood prediction using machine learning models: literature review. Water 10:1536. https://doi.org/10.3390/w10111536CrossRef

Niu W, Feng Z, Cheng C, Zhou J (2019) Forecasting daily runoff by extreme learning machine based on quantum-behaved particle swarm optimization. J Hydrol Eng 23(3). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001625

Nou MRG, Zolghadr M, Bajestan MS, Azamathulla MH (2020) Application of ANFIS–PSO hybrid algorithm for predicting the dimensions of the downstream scour hole of ski-jump spillways. Iran J Sci Technol Trans Civ Eng 45:1845–1859. https://doi.org/10.1007/s40996-020-00413-wCrossRef

Parsaie A, Haghiabi AH, Emamgholizadeh S, Azamathulla HM (2019) Prediction of discharge coefficient of combined weir-gate using ANN, ANFIS and SVM. Int J Hydrol Sci Technol 9(4)

Piri J, Mohammadi K, Shamshirband S, Akib S (2016a) Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation. Environ Earth Sci 75(3):246. https://doi.org/10.1007/s12665-015-5058-3ADSCrossRef

Pham QB, Mohammadi B, Moazenzadeh R, Heddam S, Zolá RP, Sankaran A, Gupta V, Elkhrachy I, Khedher KM, Anh DT (2023) Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms. Appl Water Sci 13:13. https://doi.org/10.1007/s13201-022-01815-zADSCrossRef

Phama BT, Le LM, Lec TT, Buid KTT, Le VM, Ly HB, Prakashe I (2020) Development of advanced artificial intelligence models for daily rainfall prediction. Atmos Res 237:104845. https://doi.org/10.1016/j.atmosres.2020.104845

Piri J, Mohammadi K, Shamshirband S, Akib S (2016b) Assessing the suitability of hybridizing the Cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation. Environ Earth Sci 75:246ADSCrossRef

Riahi-Madvar H, Dehghani M, Memarzadeh R, Gharabaghi B (2021) Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS. Water Resources Management 35:1149–1166. https://doi.org/10.1007/s11269-020-02756-5CrossRef

Rezaie-Balf M, Kisi O, Chua LH (2019) Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation. Hydrol Res 50:2. https://doi.org/10.2166/nh.2018.050CrossRef

Sahoo BB, Panigrahi B, Nanda T, Tiwari MK, Sankalp S (2023) Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models. SN Comput Sci 4(6):752. https://doi.org/10.1007/s42979-023-02246-6CrossRef

Sahoo A, Samantaray S, Paul S (2021) Efficacy of ANFIS-GOA technique in flood prediction a case study of Mahanadi river basin in India. H2Open J 4(1):137–156CrossRef

Samanataray S, Sahoo A (2021) A Comparative Study on Prediction of Monthly StreamflowUsing Hybrid ANFIS-PSO Approaches. KSCE J Civ Eng 25(10):4032–4043. https://doi.org/10.1007/s12205-021-2223-yCrossRef

Sanikhani H, Kisi O, Maroufpoor E, Yaseen ZM (2018) Temperaturebased modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theor Appl Climatol 1–14. https://doi.org/10.1007/s00704-018-2390-z

Seifi A, Soroush F (2020) Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran. Comput Electron Agric 173:105418. https://doi.org/10.1016/j.compag.2020.105418CrossRef

Seifi A, Ehteram M, Soroush F, Torabi A (2022) Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach. Eng Appl Artif Intell 114:105124. https://doi.org/10.1016/j.engappai.2022.105124

Shabani S, Samadianfard S, Sattari MT, Mosavi A, Shamshirband S, Kmet T, Várkonyi-Kóczy AR (2020) Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; comparative analysis. Atmosphere 11:66. https://doi.org/10.3390/atmos11010066ADSCrossRef

Shiri J, Dierickx W, Mohammed A, Ali B, Ghorbani A (2011) Estimating daily pan evaporation from climatic data of the State of Illinois USA using adaptive neurofuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrol Res 42(6):491–502. https://doi.org/10.2166/nh.2011.020CrossRef

Singh A, Singh RM, Kumar AR, Kumar A, Hanwat S, Tripathi VK (2021) Evaluation of soft computing and regression-based techniques for the estimation of evaporation. J Water Clim Change 12:1. https://doi.org/10.2166/wcc.2019.101CrossRef

Soroush F, Fathian F, Khabisi FH et al (2020) Trends in pan evaporation and climate variables in Iran. Theor Appl Climatol 142:407–432. https://doi.org/10.1007/s00704-020-03262-9ADSCrossRef

Tabbussum R, Dar AQ (2021) Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction. Environ Sci Pollut Res 28(20):25265–25282CrossRef

Terzi O (2013) Estimating daily pan evaporation using data mining process. Scientia Iranica A 20(4):1077–1084

Tikhamarine Y, Malik A, Kumar A, Souag-Gamane D, Kisi O (2019) Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrol Sci J 64:1824–1842. https://doi.org/10.1080/02626667.2019.1678750CrossRef

Wang L, Niu Z, Kisi O, Li C, Yu C (2017) Pan evaporation modeling using four different heuristic approaches. Comput Electron Agric 140:203–213. https://doi.org/10.1016/j.compag.2017.05.036CrossRef

Wang RY, Yang XG, Zhang JL, Wang DM, Liang DS, Zhang LA (2007) Study of soil water and land surface evaporation and climate on Loess Plateau in the Eastern Gansu Province. Adv Earth Sci 22:625–635. https://doi.org/10.11867/j.issn.1001-8166.2007.06.0625CrossRef

White WR, Milli H, Crabbe AD (1973) Sediment transport: an appraisal method. In: Performance of theoretical methods when applied to flume and field data. Tech. Rep. 1T119. Wallingford

Wu L, Huang G, Fan J, Ma X, Zhou H, Zeng W (2019) Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput Electron Agric 168(12):105115. https://doi.org/10.1016/j.compag.2019.105115CrossRef

Yaseen ZM, Ebtehaj I, Bonakdari H, Deo RC, Danandeh MA, Mohtar WHM, Diop L, El-Shafie A, Singh VP (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol 554:. https://doi.org/10.1016/j.jhydrol.2017.09.007

Yaseen ZM, Al-Juboori AM, Beyaztas U, Al-Ansari N, Chau KW, Qi K, Ali M, Salih SQ, Shahid S (2020) Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models. Eng Applic Comput Fluid Mech 14(1):70–89. https://doi.org/10.1080/19942060.2019.1680576CrossRef

Zhao G, Gao H (2019) Estimating reservoir evaporation losses for the United States: fusing remote sensing and modeling approaches. Remote Sens Environ 226(1):109–124. https://doi.org/10.1016/j.rse.2019.03.015ADSCrossRef

Zhou Y, Guo S, Chang FJ (2019) Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts”. J Hydrol 570:343–355CrossRef

Zounemat-Kermani M, Kisi O, Piri J, Meymand AM (2019) Assessment of artificial intelligence–Based models and metaheuristic algorithms in modeling evaporation. J Hydrol Eng Arch 24(10). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001835

Titel: Prediction of reservoir evaporation considering water temperature and using ANFIS hybridized with metaheuristic algorithms
verfasst von: Boudjerda Marouane
Mohammed Abdullahi Mu’azu
Andrea Petroselli‬
Publikationsdatum: 24.01.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 2/2024
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-024-01223-8

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