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Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters. [ABSTRACT FROM AUTHOR]

Unsupervised clustering methods are used as data-driven models for mapping of mineral prospectivity (MPM). One of the challenging problems for modeling high potential mineral prospects using these algorithms is to select proper cluster centroids in order to locate the same geospatial objects into reasonable clusters. To overcome this kind of problems arise from traditional clustering algorithms (e.g. K-means), the combination of harmony search (HS) and artificial bee colony (ABC) metaheuristic optimization algorithms with K-means have been firstly developed in this study which can be used for selection of optimum cluster centroids for highlighting favorable targets in prospecting stage of mineral explorations. This stage is critically important for exploration of undiscovered mineral deposits, because the time and cost are two substantial factors in regional-scale exploration programs. This study stated that the applying hybrid data-driven clustering models (e.g., HS-KM and ABC-KM) are operational tools for identifying high-favorable areas linked to porphyry Cu systems in prospecting stage. Moreover, the results demonstrated that the properly selection of geospatial exploration features has strong impact on the modeling of mineral prospectivity in Kerman porphyry belt, Iran and cause significant decrease in systemic uncertainty linked to MPM. [ABSTRACT FROM AUTHOR]

As a frequent natural disaster, landslides incur significant economic and human losses worldwide. The main idea of this paper is to propose novel integrative models for landslide susceptibility evaluation in a prone area of Chaharmahal va Bakhtiari Province in Iran. To do this, four metaheuristic techniques, namely chimp optimization algorithm (ChOA), crow search algorithm (CSA), satin bowerbird optimization (SBO), and water cycle algorithm (WCA) are used to supervise the training of an artificial neural network (ANN). A spatial database is created by 170 historical landslides and 14 most common conditioning factors. The models are optimized in terms of hyper-parameters and elite ones are then used to produce the susceptibility maps. During the training phase, all models could acquire a reliable understanding of the landslide pattern. The obtained accuracy index of area under the receiving operating characteristic curve (AUROC) showed that the maps produced by the WCA-ANN (AUROC = 0.925) and SBO-ANN (AUROC = 0.900) are more accurate than those of the ChOA-ANN (AUROC = 0.851) and CSA-ANN (AUROC = 0.855). Considering this assessment, the suggested hybrid models are reliable enough for landslide susceptibility modeling. Accordingly, the generated susceptibility map can be used for urban planning and generating alert systems over the studied area. [ABSTRACT FROM AUTHOR]