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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions. Plain Language Summary: Floods have the potential to negatively affect human wellbeing, infrastructures and the natural environment. Identifying regions prone to flooding is essential in avoiding such catastrophes. In the current study, standard and optimized convolutional neural network models are used to generate maps identifying the regions of Sweden with highest probability of flooding. Various topographic, hydrological, and anthropogenic factors are taken into account for the modeling. The analysis reveals numerous areas in Sweden prone to flooding, especially in the northern, central, and southeastern parts of the country. Malmo (the third largest city in Sweden) and some areas of Stockholm (capital) are the cities most susceptible to flooding. Additionally, considerable extensions of Sweden's roadways and railways might be impacted by floods. Accurate flood susceptibility mapping is required to assist policymakers and urban planners in implementing measures aimed at mitigating flood vulnerability. Furthermore, the findings show the ability of deep learning models for detecting flood‐susceptible areas, which can be implemented worldwide to improve flood management strategies and protect lives. Key Points: Deep learning model of convolutional neural network (CNN) was optimized and improved with gray wolf optimizer (GWO) and imperialist competitive algorithm (ICA) to detect flood‐prone areasDuring both the training and testing phases, the CNN‐GWO model demonstrated superior performance compared to CNN‐ICA and the standalone CNN modelThe results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility [ABSTRACT FROM AUTHOR]

In the present study, three widely used modeling approaches: (1) sediment rating curve (SRC) and optimized OSRC, (2) machine learning models (ML) (random forest (RF) and Dagging-RF (DARF)) and (3) the semi-physically based soil and water assessment tool (SWAT) are applied to predict suspended sediment load (Qs) at the Talar watershed in Iran. Various graphical and quantitative methods were used to evaluate the goodness of fit. Results indicated that the RF model had the best prediction power in the training phase, while the dagging-RF hybrid algorithm outperformed all other models in the validation phase. The OSRC, RF and DA-RF had ‘very good’ performances based on the NSE in the validation phase, SRC showed ‘good’ performance, while the predicted values using SWAT were ‘satisfactory’. Our results suggest that the OSRC and ML models are more suitable for prediction of Qs in study catchments with poor data availability. [ABSTRACT FROM AUTHOR]

Current study presents the boosting of two base models, including a Heuristic Search Algorithm for finding the k shortest paths (K*) and an alternating model tree (AM Tree) through combining bagging (BA), dagging (DA), and random subspace (RS) hybridize models, to predict monthly suspended sediment load (SSL) in subtropical monsoon climatic regions. In this study, the rainfall (R), streamflow (Q), water level (W), and SSL with various lag-times were fed into the model as inputs. The study found that each input scenario has a unique ability to predict SSL. Different input scenario both manually and automatically derived to improve the modeling performance and reveal impact of the potential variable on the SSL modeling. Model fit outcomes finally were evaluated using graphical and statistical performance metrics. The most effective input scenario is the one that includes all possible input variables. The study also revealed that the hybridized ensemble models outperformed standalone model. The hybrid model of BAK* performed superior to other models. While the standalone model of K* had the lowest prediction ability. Hybridized ensemble models assist in capturing extreme SSL values, especially the maximum SSL value. Results suggest that BA-K* model is superior to other models with R² = 0.80, RMSE = 772.94 Kg/s, PBIAS = 0.06, and NSE = 0.79. The reliability analysis also confirmed that the BA-K* model provided the optimal performance. [ABSTRACT FROM AUTHOR]

Land subsidence (LS), which mainly results from poor watershed management, is a complex and nonlinear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road or fault, rainfall, geology and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANNand GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR deformation map, 39%, 20%, 25%, 13% and 3% of the investigated areas are subject to a yearly LS of -1 to -2.5, -2.5 to -5, -5 to -7.5, -7.5 to -10 and -10 to -20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km² ) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future. [ABSTRACT FROM AUTHOR]