Parameters and methods used in flood susceptibility mapping: a review

Kaya C. M., DERİN CENGİZ L.

JOURNAL OF WATER AND CLIMATE CHANGE, cilt.14, sa.6, ss.1935-1960, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 14 Sayı: 6
  • Basım Tarihi: 2023
  • Doi Numarası: 10.2166/wcc.2023.035
  • Dergi Adı: JOURNAL OF WATER AND CLIMATE CHANGE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Geobase, Veterinary Science Database, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1935-1960
  • Anahtar Kelimeler: flood susceptibility mapping (FSM), metaheuristic optimization algorithm, parameters, statistical methods, SUPPORT VECTOR MACHINE, ANALYTIC HIERARCHY PROCESS, LOGISTIC-REGRESSION MODEL, DECISION-MAKING APPROACH, WEIGHTS-OF-EVIDENCE, LANDSLIDE SUSCEPTIBILITY, BIVARIATE STATISTICS, SPATIAL PREDICTION, FREQUENCY RATIO, CONDITIONING FACTORS
  • Ankara Üniversitesi Adresli: Evet

Özet

A correct understanding of the parameters and methods used in flood susceptibility mapping (FSM) is critical for identifying the strengths and limitations of different mapping approaches, as well as for developing methodologies. In this study, we examined scientific publications in the literature using WoS. Although the number of methods used is quite high (about 160 with submethods), the number of parameters used in these methods varies, with a maximum of 21 and a minimum of 5 parameters preferred. It was found that the most commonly used parameter has a preference rate of 97%, but there is no common parameter in 100% of the studies. The methods used for determining flood susceptibility include multi-criteria decision-making (MCDM) methods, physically based hydrological models, statistical methods, and various soft computing methods. Although the use of traditional statistical methods and MCDM methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning. In the reviewed studies, it was observed that machine learning, fuzzy logic, metaheuristic optimization algorithms, and heuristic search algorithms, which are soft computing methods, have been widely used in FSM in recent years.