Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad Plain, Iran)

© 2018 by the authors. It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions

Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies

Analytical evaluation of output current ripple amplitude in three-phase three-level inverters

Nowadays, three-phase multilevel inverters are widely employed in medium and high-power applications, increasing the power ratings, improving the output voltage quality and reducing the conducted electromagnetic interferences. Despite of numerous pulse-width modulation (PWM) techniques have been developed for multilevel inverters, a detailed analysis of the output current ripple amplitude has not been reported yet. In this study, the peak-to-peak current ripple distribution over a fundamental period is analysed in details specifically for three-level three-phase voltage source inverters for both motor-load and grid-connected applications. In particular, the peak-to-peak amplitude of the current ripple is determined analytically as a function of the modulation index. The centred PWM strategy is considered in all the developments, implemented either by carrier-based or space vector (SV) PWM methods. With this modulation, the dc bus utilisation is maximised in a simple and effective way, and a nearly-optimal behaviour is obtained to minimise the current ripple rms. The results obtained in different cases and sub-cases identified in the proposed analytical approach are verified by experimental tests with reference to three-phase three-level neutral-point clamped configuration

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

Water Resources Management Through Flood Spreading Project Suitability Mapping Using Frequency Ratio, k-nearest Neighbours, and Random Forest Algorithms

Lack of water resources is a common issue in many countries, especially in the Middle East. Flood spreading project (FSP) is an artificial recharge technique, which is generally suggested for arid and semi-arid areas with two major aims including (1) flood mitigation and (2) artificial recharge of groundwater. This study implemented three state-of-the-art popular models including frequency ratio (FR), k-nearest neighbours (KNN), and random forest (RF) for determining the suitability of land for FSP. At the first step, suitable areas for FSP were identified according to the national guidelines and the literature. The identified areas were then verified by multiple field surveys. To produce FSP land suitability maps, several FSP conditioning factors such as topographical (i.e. slope, plan curvature, and profile curvature), hydrogeological (i.e. transmissivity, aquifer thickness, and electrical conductivity), hydrological (i.e. rainfall, distance from rivers, river density, and permeability), lithology, and land use were considered as input to the models. For the FR modelling, classified layers of the aforementioned variables were used, while their continuous layers were implemented in the KNN and RF algorithms. At the last step, receiver operating characteristic (ROC) curve was used to assess the ability and accuracy of the applied algorithms. Based on the findings, the area under the curve of ROC for the RF, KNN, and FR models was 97.1, 94.6, and 89.2%, respectively. Furthermore, transmissivity, slope, aquifer thickness, distance from rivers, rainfall, and electrical conductivity were recognized as the most influencing factors in the modelling procedure. The findings of this study indicated that the application of RF, KNN, and FR can be suggested for identification of suitable areas for FSP establishment in other regions