Cumulative Infiltration and Infiltration Rate Prediction Using Optimized Deep Learning Algorithms: A Study in Western Iran

Study region: Sixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focus: Soil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. New Hydrological Insights for the Region: Finding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates

Convolutional neural network approach for spatial prediction of flood hazard at national scale of Iran

Iran experiences frequent destructive floods with significant socioeconomic consequences. Quantifying the accurate impacts of such natural hazards, however, is a complicated task. The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a flood susceptibility map for Iran. A total of 2769 records were collected from flood locations across the entire country; we divided this data set into two groups using a cross-validation technique. The first group, used as a training data set, was constructed from 70% of the data set and was used for model building. The second group, used as a testing data set, was constructed from the remaining 30% of the records and used for validation. Ten flood conditioning factors, slope, altitude, aspect, plan curvature, profile curvature, rainfall, geology, land use, distance from roads, and distance from rivers, were identified and used in the modeling process. The area under the prediction-rate curve was used for model evaluation, with results showing that the flood susceptibility map has an acceptable accuracy of 75%. The results also indicated that approximately 12% and 3% of the country are highly and very highly susceptible to future flooding events, respectively. Moreover, 29% and 49% of Iran's cities are located in areas with high and very high susceptibility to future flooding hazards. The most effective approaches to flood mitigation are preventing urban expansion and new construction in highly to very highly flood-prone areas as well as watershed management plans and constructing flood control structures according to the topographical characteristics of the catchment.</p

SEVUCAS: A Novel GIS-Based Machine Learning Software for Seismic Vulnerability Assessment

Since it is not possible to determine the exact time of a natural disaster’s occurrence and the amount of physical and financial damage on humans or the environment resulting from their event, decision-makers need to identify areas with potential vulnerability in order to reduce future losses. In this paper, a GIS-based open source software entitled Seismic-Related Vulnerability Calculation Software (SEVUCAS), based on the Step-wise Weight Assessment Ratio Analysis (SWARA) method and geographic information system, has been developed to assess seismic vulnerability by considering four groups of criteria (i.e., geotechnical, structural, socio-economic, and physical distance to needed facilities and away from dangerous facilities). The software was developed in C# language using ArcGIS Engine functions, which provide enhanced visualization as well as user-friendly and automatic software for the seismic vulnerability assessment of buildings. Weighting of the criteria (indicators) and alternatives (sub-indicators) was done using SWARA. Also, two interpolation methods based on a radial basis function (RBF) and teaching–learning-based optimization (TLBO) were used to optimize the weights of the criteria and the classes of each alternative as well. After weighing the criteria and alternatives, the weighted overlay analysis was used to determine the final vulnerability map in the form of contours and statistical data. The difference between this software and similar ones is that people with a low level of knowledge in the area of earthquake crisis management can use it to determine and estimate the seismic vulnerabilities of their houses. This visualized operational forecasting software provides an applicable tool for both government and people to make quick and correct decisions to determine higher priority structures for seismic retrofitting implementation

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations

A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning

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. ANN- and 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

The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Abstract Machine learning models have attracted much research attention for groundwater potential mapping. However, the accuracy of models for groundwater potential mapping is significantly influenced by sample size and this is still a challenge. This study evaluates the influence of sample size on the accuracy of different individual and hybrid models, adaptive neuro-fuzzy inference system (ANFIS), ANFIS-imperial competitive algorithm (ANFIS-ICA), alternating decision tree (ADT), and random forest (RF) to model groundwater potential, considering the number of springs from 177 to 714. A well-documented inventory of springs, as a natural representative of groundwater potential, was used to designate four sample data sets: 100% (D₁), 75% (D₂), 50% (D₃), and 25% (D₄) of the entire springs inventory. Each data set was randomly split into two groups of 30% (for training) and 70% (for validation). Fifteen diverse geo-environmental factors were employed as independent variables. The area under the operating receiver characteristic curve (AUROC) and the true skill statistic (TSS) as two cutoff-independent and cutoff-dependent performance metrics were used to assess the performance of models. Results showed that the sample size influenced the performance of four machine learning algorithms, but RF had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that RF (AUROC = 90.74–96.32%, TSS = 0.79–0.85) had the best performance based on all four sample data sets, followed by ANFIS-ICA (AUROC = 81.23–91.55%, TSS = 0.74–0.81), ADT (AUROC = 79.29–88.46%, TSS = 0.59–0.74), and ANFIS (AUROC = 73.11–88.43%, TSS = 0.59–0.74). Further, the relative slope position, lithology, and distance from faults were the main spring-affecting factors contributing to groundwater potential modelling. This study can provide useful guidelines and a valuable reference for selecting machine learning models when a complete spring inventory in a watershed is unavailable

Toward the development of deep-learning analyses for snow avalanche releases in Mountain regions

Abstract Snow avalanches impose a considerable threat to infrastructure and human safety in snow bound mountain areas. Nevertheless, the spatial prediction of snow avalanches has received little research attention in many vulnerable parts of the world, particularly in developing countries. The present study investigates the applicability of a stand-alone convolutional neural network (CNN) model, as a deep-learning approach, along with two metaheuristic algorithms including grey wolf optimization (CNN-GWO) and imperialist competitive algorithm (CNN-ICA) in snow avalanche modeling in the Darvan watershed, Iran. The analysis was based on thirteen potential drivers of avalanche occurrence and an inventory map of previously documented avalanche occurrences. The efficiency of models’ performance was evaluated by Area Under the Receiver Operating Characteristic curve (AUC) and the Root Mean Square Error (RMSE). The CNN-ICA model yielded the highest accuracy in both training (AUC= 0.982, RMSE =0.067) and validation (AUC= 0.972, RMSE =0.125) steps, followed by the CNN-GWO model (AUC of 0.975 for training, RMSE of 0.18 for training, AUC of 0.968 for validation, RMSE of 0.157 for validation). However, the standalone CNN model showed lower goodness-of-fit (AUC= 0.864, RMSE =0.22) and predictive performance (AUC= 0.811, RMSE =0.330). The approach utilized in this study is broadly applicable for identifying areas where avalanche hazard is likely to be high and where mitigation measures or corresponding land use planning should be prioritized

Development of novel hybridized models for urban flood susceptibility mapping

Abstract Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services