Regional flood frequency analysis using the FCM-ANFIS algorithm : a case study in south-eastern Australia

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38-60%) than the SC (RE of 44-69%) and GP (RE of 42-78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique

The Relationship between Surface Water Quality and Watershed Characteristics

The healthy water resources are necessary and essential prerequisite for environmental protection and economic development, political, social and cultural rights of Iran. In this research, water quality parameters i.e. total dissolved solids (TDS), sodium absorption rate (SAR), electrical conductivity (EC), Na+, Cl-, CO32-, K+, Mg2+, Ca2+, pH, HCO3- and SO42- during 2010-2011 were obtained from Iranian Water Resources Research Institute in water quality measurement stations on Mazandaran province, Iran. Then, the most important catchment characteristics (area, mean slope, mean height, base flow index, annual rainfall, land cover, and geology) were determined on water quality parameters using stepwise regression via backwards method in the 63 selected rivers. The results showed that sodium absorption rate (SAR), total dissolved solids (TDS), electrical conductivity (EC), Na+ and Cl- parameters are strongly linked to geology characteristics, while K+, Mg2+ and Ca2+ cations is linked to rainfall and geology characteristics. pH and HCO3- are related to area, rainfall, land cover and geology characteristics, CO32- is related to area, rainfall, rangeland area and geology characteristics and SO42- is related to area, rainfall, range and bar land area and geology characteristics. Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modeling the selected catchment characteristics and water quality parameters. The ANFIS models have a high Nash-Sutcliffe model efficiency coefficient (NSE)  and low root mean squares error (RMSE) to estimate water quality parameters

Comparing performance of ANN and SVM methods for regional flood frequency analysis in South-East Australia

Design flood estimations at ungauged catchments are a challenging task in hydrology. Regional flood frequency analysis (RFFA) is widely used for this purpose. This paper develops artificial intelligence (AI)-based RFFA models (artificial neural networks (ANN) and support vector machine (SVM)) using data from 181 gauged catchments in South-East Australia. Based on an independent testing, it is found that the ANN method outperforms the SVM (the relative error values for the ANN model range 33-54% as compared to 37-64% for the SVM). The ANN and SVM models generate more accurate flood quantiles for smaller return periods; however, for higher return periods, both the methods present a higher estimation error. The results of this study will help to recommend new AI-based RFFA methods in Australia

The monetary facilities payment for ecosystem services as an approach to restore the Degraded Urmia Lake in Iran.

peer reviewedThis study analyzed the potential use of Payment for Ecosystem Services (PES) as a strategy for improving water supply management. This study focused on the Siminehroud Sub-basin due to its high importance to the Basin of Urmia Lake (UL). Siminehroud is the second provider of water (by volume) to Urmia Lake. To evaluate the technical and economic feasibility of a PES scheme, the current land use map was extracted using satellite imagery. In addition, the two algorithms of Support Vector Machines (SVMs) and Maximum Likelihood (ML) are used for Landsat images classification, rather than analyzing the relationship between land use and ecosystem services. Then, the most relevant ecosystem services provided in the region were evaluated using the Benefit Transfer Method. In the last step, by designing and implementing a survey, on the one hand, the local farmers' Willingness to Accept (WTA) cash payments for reducing the area they cultivate, and on the other hand, the farmers' Willingness to Pay (WTP) for managing the water consumption were determined. The results illustrated that the WTA program is more acceptable among the beneficiaries. It is also notable that this program needs very high governmental funding. Furthermore, the results of the program indicate that the land area out of the cultivation cycle will gradually increase while the price of agricultural water will also increase

Artificial intelligence-based regional flood frequency analysis methods : a scoping review

Flood is one of the most destructive natural disasters, causing significant economic damage and loss of lives. Numerous methods have been introduced to estimate design floods, which include linear and non-linear techniques. Since flood generation is a non-linear process, the use of linear techniques has inherent weaknesses. To overcome these, artificial intelligence (AI)-based non-linear regional flood frequency analysis (RFFA) techniques have been introduced over the last two decades. There are limited articles available in the literature discussing the relative merits/demerits of these AI-based RFFA techniques. To fill this knowledge gap, a scoping review on the AI-based RFFA techniques is presented. Based on the Scopus database, more than 1000 articles were initially selected, which were then screened manually to select the most relevant articles. The accuracy and efficiency of the selected RFFA techniques based on a set of evaluation statistics were compared. Furthermore, the relationships among countries and researchers focusing on AI-based RFFA techniques are illustrated. In terms of performance, artificial neural networks (ANN) are found to be the best performing techniques among all the selected AI-based RFFA techniques. It is also found that Australia, Canada, and Iran have published the highest number of articles in this research field, followed by Turkey, the United Arab Emirates (UAE), India, and China. Future research should be directed towards identification of the impacts of data quantity and quality, model uncertainty and climate change on the AI-based RFFA techniques

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Flood mapping using HEC-RAS hydraulic model in part of Khorramabad watershed

Flood prone area identification in the watersheds is one of the basic solutions for destructive flood control and mitigation. Flood mapping is one of the best methods for flood prone area planning and identifying. For this purpose, flow boundary conditions, peak instantaneous discharge with different return periods, cross sections and their distance and roughness coefficients for each cross section were entered to HEC-RAS hydraulic model in Khorramabad watershed located in Lorestan province, Iran, and this model was then run and flood water surface profile at different return periods were estimated. The results obtained from flood mapping showed that flood prone area in return period of 2-year with 145.125 m3s-1 and return period of 100-year with 553.781 m3 s-1 effect 8.63 and 10 km2 in area, respectively. So that about 4.4 km2 of the total rain-fed farming area, 2.4 km2 of total rangeland area and 1.4 km2 of total residential, 1.6 road area and 0.2 km2 abandoned effect by flood in return period of 100-year. Similarly for other flood return periods was also observed that the most flood prone areas are related to rain-fed farming, rangeland, road, residential area and abandoned the land

The effect of different sampling schemes on estimation precision of snow water equivalent (SWE) using geostatistics techniques in a semi-arid region of Iran

The aim of this study is to compare the effect of two sampling patterns: systematic sampling and Latin hypercube sampling (LHS), on estimation precision of snow water equivalent (SWE), and also comparing different geostatistics methods of kriging, cokriging and radial basin functions for mapping SWE. To achieve the study purpose, the semi-arid mountainous watershed of Sohrevard in Zanjan Province of Iran was selected. Snow depth in 150 points with systematic sampling and 150 points with LHS sampling and snow density in 18 points were randomly measured. In addition, SWE was calculated in the study area, and its map was derived based on both the sampling methods using geostatistical techniques. The results showed that the accuracy of the SWE map using LHS was higher than systematic sampling. According to the most statistical indicators, in both methods of sampling, accuracy of mapping using regular spline was better than other methods

Flood hazard zoning using HEC-RAS Hydraulic Model and ArcGIS (Case Study: CheshmehKileh River in Tonekabon County)

Flood is defined as “streamflow with high discharge which spreads temporary on the lands nearby the main river” and consequently there will be some risks. Due to proper conditions, most of the economic and social activities take place in these areas. Therefore, it is important to determine flood zonation due to flood discharge with different return periods in these areas. In this regard, the Cheshmehkileh River located in Tonekabon Township has vast river boundary zones and most of the economic and social activities are carried out in its neighborhood, which is susceptible to flood hazard according to flood conditions and occurred floods. The cross sections were derived using the digital elevation model (DEM) with scale of 1:1000 in Arc/GIS environment and HEC-GeoRAS extension and the obtained outputs were entered into HEC-RAS hydraulic software. Manning roughness coefficients and flood discharge with five return periods of 2, 5, 10, 25, 50 and 100-year were entered into HEC-RAS model and water surface profile was computed  in flow path and flood zonation for different return periods was derived using the HEC-RAS output information through geographical information system. The obtained results showed that the longer the return period, the wider the surface affected by flood. Also, the highest flood area is related to agricultural land use with an area of 6.24 ha and after residential land uses with an area of 3.94 ha, forest with an area of 2.9 ha and orchard of an area of 0.8 ha were located at the following ranks