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

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

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

Lipid quality in benni (Barbus sharpeyi) fillets during ice storage

Abstract This research was conducted to evaluate qualitative changes of Benni Fish (Barbus Sharpeyi) during its maintenance in ice storage for 20 days. To do so, chemical spoilage indicators including peroxide (PV), thiobarbituric acid (TBA), free fatty acids(FFA), total lipid (TL), moisture (M), heme iron (HI), and also organoleptic parameters (tissue, gill appearance, gill smell, general appearance, and eyes) were measured. Fat quality of sample fish (in terms of oxidative and hydrolytic rancidity) showed a significant reduction during the maintenance period (p<0.05). Peroxide index changes from 3.73 to 7.52 (meq/kg) and TBA from 0.5 to 6.6 (mg MDA/kg) was recorded as markers of oxidative spoilage and FFA changes from 2.05 to 6.58 (expressed as % of oleic acid) were recorded as indicator of hydrolytic rancidity. Each one of sensory tests were rated as excellent to good until the fourth day and their quality was acceptable until the tenth day and then organoleptic results dropped significantly. In general, the best time of fish Shelf life in ice storage was determined to be 7 to 10 days

flood hazard susceptibility modeling in te Kan Watershed

Dataset for modeling flood hazard in the Kan watershed, Ira

Performance Assessment of Five Water Balance Models for Runoff Simulation in the Gorganrood Watershed

Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences.  The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCE-UA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period