Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model - Barzegar, Rahim; Aalami, Mohammad Taghi; Adamowski, Jan (Stochastic Environmental Research and Risk Assessment, 2020)

A participatory system dynamics modeling approach to facilitate collaborative flood risk management: A case study in the Bradano River (Italy) - Perrone, Antonio; Inam, Azhar; Albano, Raffaele; Adamowski, Jan; Sole, Aurelia (Journal of Hydrology, 2020)

Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones - Barzegar, Rahim; Asghari Moghaddam, Asghar; Norallahi, Shahrokh; Inam, Azhar; Adamowski, Jan; Alizadeh, Mohammad Reza; Bou Nassar, Jessica (Groundwater, 2020)
Abstract The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO 3 ) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin‐Shahr plain, Iran. Twenty‐two groundwater samples were collected from wells in the study area, and their respective NO 3 concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO 3 concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R 2 of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R 2 value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R 2 value to the highest degree. , Article impact statement : The results showed that both linear and nonlinear methods are useful in modifying the ratings and weights of the DRASTIC method for assessing the groundwater vulnerability.

Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania - Albano, Raffaele; Samela, Caterina; Crăciun, Iulia; Manfreda, Salvatore; Adamowski, Jan; Sole, Aurelia; Sivertun, Åke; Ozunu, Alexandru (Water, 2020)
Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.