Check dams are widely used watershed management measures for reducing flood peak discharge and sediment transport, and increasing lag time and groundwater recharge throughout the world. However, identifying the best suitable sites for check dams within the stream networks of various watersheds remains challenging. This study aimed to develop an open-source software with user-friendly interface for screening the stream network possibilities and identifying and guiding the selection of suitable sites for check dams within watersheds. In this developed site selection software (SSS), multi-criteria decision analysis (MCDA) was integrated into geographic information systems (GIS), which allowed for numerous spatial data of the multiple criteria to be relatively simply and visually processed. Different geomorphometric and topo-hydrological factors were considered and accounted for to enhance the SSS identification of the best locations for check dams. The factors included topographic wetness index (TWI), terrain ruggedness index (TRI), topographic position index (TPI), sediment transport index (STI), stream power index (SPI), slope, drainage density (DD), and stream order (SO). The site identification performance of the SSS was assessed using the receiver operating characteristic (ROC) curve method, with results for the case study example of the Poldokhtar watershed in Iran showing excellent performance and identifying 327 potential sites for efficient check dam construction in this watershed. The SSS tool is not site-specific but is rather general, adaptive, and comprehensive, such that it can and should be further applied and tested across different watersheds and parts of the world.

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accuracy for estimating vulnerable regions and taking appropriate initiatives. The Golestan Dam has faced an acute problem of gully erosion over the last decade and has adversely affected society. Here, the artificial neural network (ANN), general linear model (GLM), maximum entropy (MaxEnt), and support vector machine (SVM) machine learning algorithm with 90/10, 80/20, 70/30, 60/40, and 50/50 random partitioning of training and validation samples was selected purposively for estimating the gully erosion susceptibility. The main objective of this work was to predict the susceptible zone with the maximum possible accuracy. For this purpose, random partitioning approaches were implemented. For this purpose, 20 gully erosion conditioning factors were considered for predicting the susceptible areas by considering the multi-collinearity test. The variance inflation factor (VIF) and tolerance (TOL) limit were considered for multi-collinearity assessment for reducing the error of the models and increase the efficiency of the outcome. The ANN with 50/50 random partitioning of the sample is the most optimal model in this analysis. The area under curve (AUC) values of receiver operating characteristics (ROC) in ANN (50/50) for the training and validation data are 0.918 and 0.868, respectively. The importance of the causative factors was estimated with the help of the Jackknife test, which reveals that the most important factor is the topography position index (TPI). Apart from this, the prioritization of all predicted models was estimated taking into account the training and validation data set, which should help future researchers to select models from this perspective. This type of outcome should help planners and local stakeholders to implement appropriate land and water conservation measures.

The present work sets out to 1) evaluate the corrosion-scaling potential of groundwater resources for the industrial sector as well as to 2) examine groundwater chemical parameters for the agricultural sector in the Piranshahr Watershed in the West Azerbaijan province, Iran, using geostatistical analyses and the Wilcox diagram in a GIS environment. A total of 145 spring locations as representatives of groundwater potentiality were recorded by a handheld GPS device and the corrosion and scaling potential states were further scrutinized. The latter was carried out on the basis of examining the chemical parameters at each sample location including alkalinity, pH, temperature, Na+, Ca++, Mg++, TH, HCO 3 - , Co 3 - 2 , Cl - , SO 4 - 2 , Electrical conductivity, and Total dissolved solids. The corrosion and scaling potential of groundwater was then evaluated by using Langelier saturation index (LSI), Larson-Skold index (LS), Ryznar stability index (RSI), Aggressive index (AI), and Puckorius scaling index (PSI). Also, the groundwater quality state for agriculture was assessed by the Wilcox diagram on the basis of Electrical conductivity and Sodium adsorption ratio parameters. The provided data are beneficial for researchers, policymakers, and authorities for taking pragmatic actions. Also, the compiled data can be used in the context of corrosion/scaling and groundwater quality assessment can be generalized around the world.