Evaluating the Multifunctional Resilience of River Health to Human Service Demand: A Comparison among Fuzzy Logic, Entropy and AHP Based MCDM Models RAJ BHATTACHARYA ( [email protected] ) Vidyasagar University Nilanjana Das Chatterjee Vidyasagar University Kousik Das Vidyasagar University Research Article Keywords: Harmonious relationship, Human service demands (HSDs), River health connotation, Multifunctional threshold, Multi-criteria decision matrix (MCDM) Posted Date: July 12th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-621760/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Title: 2 Evaluating the multifunctional resilience of river health to human service demand: A comparison among 3 fuzzy logic, entropy and AHP based MCDM models 4 Dr. Raj Kumar Bhattacharya 5 PhD 6 Department of Geography 7 Vidyasagar University 8 Midnapore, West Bengal, India 9 Pin: 721102 10 Phone: +917797232031 11 Email: [email protected] 12 ORCID- https://orcid.org/0000-0002-1102-0088 13 Other authors 14 Dr. Nilanjana Das Chatterjee 15 Professor 16 Department of Geography 17 Vidyasagar University 18 Midnapore, West Bengal, India 19 Pin: 721102 20 Email: [email protected] 21 ORCID- https://orcid.org/0000-0001-9436-2173 22 Mr. Kousik Das 23 PhD Research Scholar (JRF) 24 Department of Geography 25 Vidyasagar University 26 Midnapore, West Bengal, India 27 Pin: 721102 28 Email: [email protected] 29 ORCID- https://orcid.org/0000-0001-9948-1577 30 Abstract 31 Ecosystem services of river for human beings are guided by harmonious relationship; however over growing 32 human service demands (HSDs) are leading to deteriorate the river health connotation. In this study, an 33 assessment index system of monsoonal quarried river health including twenty indicators consisting of riparian, 34 morphological, hydroecological and social structure were established to detect the multifunctional threshold of 35 river system integrity in respect to HSDs at upper (US), middle (MS), lower segments (LS) of Kangsabati River 36 using fuzzy logic, AHP and entropy based multi-criteria decision matrix (MCDM) methods i.e. 37 vlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), simple additive weighing (SAW), complex 38 proportion assessment of alternatives (COPRAS), weighted aggregated sum product assessment (WASPAS), 39 technique for order preference by similarity to ideal solution (TOPSIS). The results revealed that overall 40 indicators performance is generally healthy in MS, medium health level in US but mostly unhealthy in LS; and 41 AHP and fuzzy MCDM methods assigned as high priority rank to MS, medium rank to US and least rank to LS, 42 while entropy MCDM methods gives highest rank to US, medium rank to MS and least rank to LS, respectively. 43 According to model validation performances, fuzzy and AHP MCDM methods are signified to HSDs, and 44 results are closer to real problems, whereas entropy MCDM methods are rationalized to harmonic relationship 45 of riverine system. With the acceptability of AHP SAW and WASPASS, it can be concluded that over HSDs is 46 main culprit for river health degradation, and research results are identified the sick sites for ecological 47 restoration. 48 Keywords: Harmonious relationship, Human service demands (HSDs), River health connotation, 49 Multifunctional threshold, Multi-criteria decision matrix (MCDM) 50 Introduction 51 River health is one kind of metaphor likes human health and veterinary health, which is indicated to overall 52 status of a river in particularly ecological function and biodiversity required to meet enough expectation of 53 burgeoning human social needs along with sustainable development (Meyer 1997; Ma et al. 2019). 54 Contrastingly, human satisfaction, response and physical, chemical, biological resilience capacities are 55 controlled the river’s well being state (Fairweather 1999; Sadat et al. 2019). During twenty first century, river 56 health including river ecology and its environment has become prime concern caused by abruptly socio- 57 economic development, and several constructions across the river for flow regulation likes dam, reservoir, 58 barrages, bridges, weirs etc.(Gain and Glupponi 2015; Restrepo et al. 2018). Based on socio-economic and 59 cultural functions of rivers, association of river health has become more accelerated from river ecosystem to 60 include of socio-economic and cultural factors with the following of river ecosystem integrity and human 61 service demand (HSD) (Von et al. 2017; Cheng et al. 2018; Vollmer et al. 2018). Many research works have 62 been already done about the river health connotation (RHC), which indicates river ecosystem as a more dynamic 63 process involving more constant changes after the crossing of autogenic level, and then turned to socialize with 64 the corresponding of HSDs (Luo et al. 2018). With the understanding of harmonious relationship between 65 dynamic river ecosystem and HSDs, assessment techniques play big role to determine the resilience of 66 connotation of the river health with the ensuring of scientific management of rivers. In this context, several 67 theory and methods are employed with continuously revised and enriched to measure the RHC (Alemu et al. 68 2018). Entire assessment approaches are based on three different studies i.e. (1) single based studies like 69 biological, floral species and faunal species, (2) river ecological function based studies like plant respiration, 70 evapo-transpiration and photosynthesis rates, and (3) composite indices based studies like water quality index, 71 macro invertebrate indices (Sing and Saxena 2018; Sadat et al. 2019). Recently, several indices like biological 72 integrity (Petesse et al., 2016; Wellemeyer et al. 2018; Yang et al. 2018), pollution overall index (Sargaonkar 73 and Deshpande 2003), river pollution index (Mupenzi et al. 2017), multi-metric assessment index (Shi et al. 74 2017), ecological quality index (Sing and Saxena 2018) were widely used to assess the RHC. 75 Moreover, various methods such as index of stream condition assigning from five crucial components i.e. 76 physical form, hydrology, streamside zone, aquatic life and water quality in Australian (Ladson et al. 1999), 77 rapid bio assessment protocols in USA (Oliveria et al. 2011), river habitat survey method in England (Cunha et 78 al. 2015) and EU water framework directive (Baatrup-Pedersen et al. 2018; Richter et al. 2016) were effectively 79 adopted to determine the river ecological integrity in many developed country in respect to individual national 80 condition (Luo et al. 2018). On the other hand, several mathematical approaches like multivariate analysis 81 (Chau and Muttil 2007), analytic hierarchy process (Saaty 1980), artificial neural network (Xie and Cheng 82 2006), data envelopment analysis (Zhao et al. 2006), fuzzy comprehensive assessment (Zhao and Yang 2009) 83 were already applied for river health assessment. It is point that all these methods have such significant role for 84 evaluating the multi-dimensional diversity, reliability, structural and functional complexity of river ecosystem as 85 well as RHC. However, these methods could not effectively determine the multi-index assessment and multi- 86 object decision making (Deng et al. 2015). Thus, due to the complex multifunctional threshold state of resilience 87 factors of the RHC, single evaluation method cannot trustfully performed and has not provided the effective 88 results. Consequently, RHC approach has been integrated with multi-dimensional human demands; hence non 89 linear and indeterminate methods are applied to measure this obscure multifunctional relationship (Karr 1999; 90 Norris and Thomas 1999). Therefore, application of fuzzy membership function with assessment evaluation 91 methods has been used in different perspectives such as comprehensive evaluation, scientific decisions, pattern 92 recognition etc. (Jing et al. 2000; He et al. 2011) that are helps for scientific and rational utilization to get more 93 effective evaluation results (Liu and Zou 2012). Based on fuzzy membership function, multi-criteria decision 94 making (MCDM) like fuzzy matter element method (FME) for scientific and comprehensive decision making, 95 combining grey relational analysis method (GRA) for theoretical explanation of uncertain information and 96 incomplete data samples (Chiang and Hsieh 2009), harmony degree evaluation method (HDE) for the analysis 97 of dynamic equilibrium in between HSDs and river ecosystem integrity (Zuo et al. 2016; Luo et al. 2018), were 98 successively used for achieving the different goals i.e. analysis the overall performance of every attributes under 99 river health, integration between river health and water resources management, and comparison and validation 100 amongst the model (Sadat et al. 2019). 101 Nevertheless, some MCDM models are incorporated either various uncertainties of criteria performance values 102 or uncertainties of criteria weights for river health assessment. Accordingly, most of the models cannot properly 103 detect the multifunctional threshold state of hydroecological and social attributes in respect to HSD especially in 104 RHC. Moreover, in particular of river ecosystem integration, MCDM models have not identified the attribute 105 performance in against the various environmental stresses. In this regard, this research has analyzed the several 106 hydroecological, social and geomorphic river health