Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization

Groundwater is one of the most valuable natural resources in the world (Jha et al., 2007). However, it is not an unlimited resource; therefore understanding groundwater potential is crucial to ensure its sustainable use. The aim of the current study is to propose and verify new artificial intelligence methods for the spatial prediction of groundwater spring potential mapping at the Koohdasht–Nourabad plain, Lorestan province, Iran. These methods are new hybrids of an adaptive neuro-fuzzy inference system (ANFIS) and five metaheuristic algorithms, namely invasive weed optimization (IWO), differential evolution (DE), firefly algorithm (FA), particle swarm optimization (PSO), and the bees algorithm (BA). A total of 2463 spring locations were identified and collected, and then divided randomly into two subsets: 70&thinsp;% (1725 locations) were used for training models and the remaining 30&thinsp;% (738 spring locations) were utilized for evaluating the models. A total of 13 groundwater conditioning factors were prepared for modeling, namely the slope degree, slope aspect, altitude, plan curvature, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distance from fault, distance from river, land use/land cover, rainfall, soil order, and lithology. In the next step, the step-wise assessment ratio analysis (SWARA) method was applied to quantify the degree of relevance of these groundwater conditioning factors. The global performance of these derived models was assessed using the area under the curve (AUC). In addition, the Friedman and Wilcoxon signed-rank tests were carried out to check and confirm the best model to use in this study. The result showed that all models have a high prediction performance; however, the ANFIS–DE model has the highest prediction capability (AUC&thinsp; = &thinsp;0.875), followed by the ANFIS–IWO model, the ANFIS–FA model (0.873), the ANFIS–PSO model (0.865), and the ANFIS–BA model (0.839). The results of this research can be useful for decision makers responsible for the sustainable management of groundwater resources.</p

Matrix-Based Ramanujan-Sums Transforms

In this letter, we study the Ramanujan Sums (RS) transform by means of matrix multiplication. The RS are orthogonal in nature and therefore offer excellent energy conservation capability. The 1-D and 2-D forward RS transforms are easy to calculate, but their inverse transforms are not defined in the literature for non-even function $({rm mod}~ {rm M})$. We solved this problem by using matrix multiplication in this letter

Ramanujan Sums for Image Pattern Analysis

Ramanujan Sums (RS) have been found to be very successful in signal processing recently. However, as far as we know, the RS have not been applied to image analysis. In this paper, we propose two novel algorithms for image analysis, including moment invariants and pattern recognition. Our algorithms are invariant to the translation, rotation and scaling of the 2D shapes. The RS are robust to Gaussian white noise and occlusion as well. Our algorithms compare favourably to the dual-tree complex wavelet (DTCWT) moments and the Zernike's moments in terms of correct classification rates for three well-known shape datasets

Attention on Attention for Text to Image Synthesis using Mode-Seeking Loss Function

Text to Image Synthesis is a burgeoning field that has sprung up in the research community in the last few years. Generative Adversarial Networks form the basic component of modern research as many architectures are built around this particular type. This thesis is an attempt to develop a new technique and architecture to compete with the recent state-of-the-art models. The research around the text to image synthesis is conducted using two approaches by working with the modification of the loss and then the change in architecture. In the first approach, we sample two noise vectors to generate two different output images. The model is trained by a mode-seeking loss function which maximizes the ratio of the l2-norm of difference between the two images to the l2 norm of difference between noise vectors. The second approach deals with increasing the attention between the text and the image by introducing the Attention on Attention architecture in the AttnGAN network. We find that a combination of two approaches produces good quality images and better attended on their text descriptions. The metric scores of Frechet Inception Distance and Inception Scores are used to evaluate the results. The datasets used in this research are Microsoft COCO and CUB Birds. A comparison study of the obtained results with the past state-of-the-art models is conducted and presented in this thesis