Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin

The conditions which affect the sustainability of water cause a number of serious environmental and hydrological problems. Effective and correct management of water resources constitutes an effective and important issue among scales. In this sense, a precise estimation of streamflow time series in rivers is one of the most important issues in optimal management of surface water resources. Therefore, a hybrid method combining particle swarm algorithm (PSO) and long short-term memory networks (LSTM) are proposed to predict flow with data obtained from different flow measurement stations. In this respect, the data gathered from three Flow Measurement Stations (FMS) from Zamanti and Eglence rivers located on Seyhan Basin are utilized. Besides, the proposed LSTM-PSO method is compared to an adaptive neuro-fuzzy inference system (ANFIS) and the LSTM benchmark model to demonstrate the performance achievement of proposed method. The prediction performances of the developed hybrid model and the others are tested on the determined stations. The forecasting performances of the models are determined with RMSE, MAE, MAPE, SD, and R-2 metrics. The comparison results indicated that the LSTM-PSO method provides highest results with values of R-2 (approximate to 0.9433), R-2 (approximate

Implementing PointNet for point cloud segmentation in the heritage context

Automated Heritage Building Information Modelling (HBIM) from the point cloud data has been researched in the last decade as HBIM can be the integrated data model to bring together diverse sources of complex cultural content relating to heritage buildings. However, HBIM modelling from the scan data of heritage buildings is mainly manual and image processing techniques are insufficient for the segmentation of point cloud data to speed up and enhance the current workflow for HBIM modelling. Artificial Intelligence (AI) based deep learning methods such as PointNet are introduced in the literature for point cloud segmentation. Yet, their use is mainly for manufactured and clear geometric shapes and components. To what extent PointNet based segmentation is applicable for heritage buildings and how PointNet can be used for point cloud segmentation with the best possible accuracy (ACC) are tested and analysed in this paper. In this study, classification and segmentation processes are performed on the 3D point cloud data of heritage buildings in Gaziantep, Turkey. Accordingly, it proposes a novel approach of activity workflow for point cloud segmentation with deep learning using PointNet for the heritage buildings. Twenty-eight case study heritage buildings are used, and AI training is performed using five feature labelling for segmentation namely, walls, roofs, floors, doors, and windows for each of these 28 heritage buildings. The dataset is divided into clusters with 80% training dataset and 20% prediction test dataset. PointNet algorithm was unable to provide sufficient accuracy in segmenting the point clouds due to deformation and deterioration on the existing conditions of the heritage case study buildings. However, if PointNet algorithm is trained with the restitution-based heritage data, which is called synthetic data in the research, PointNet algorithm provides high accuracy. Thus, the proposed approach can build the baseline for the accurate classification and segmentation of the heritage buildings

Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

In this paper, a new method is presented for the training of the Adaptive Neuro-Fuzzy Inference System (ANFIS). In this work, it is ensured that the best model is created by optimising the premise and consequent parameters of ANFIS by using Simulating Annealing (SA) based on an iterative algorithm. The proposed method was applied to dynamic system identification problems. The simulation results of the proposed method are compared with the Genetic algorithm (GA), Backpropagation (BP) algorithm and different methods from the literature. At the end of this study it was found that the optimisation of ANFIS parameters is more successful by using SA than by GA, BP and the other methods. (C) 2018 Elsevier B.V. All rights reserved

A comparative study of different classification algorithms on RNA-Seq cancer data

Gene mutations are the most important reason of cancer diseases, and there are different kind of causing genes across these diseases. RNA-Seq technology enables us to allow for gathering information about many genes simultaneously; hence, RNA-Seq data can be used for cancer diagnosis and classification. In this study, RNA-Seq dataset for renal cell cancer is analysed using three different developed classification methods: random forest (RF), artificial neural network (ANN) and deep learning (DL). The genes in our dataset are related to the following cancer types: kidney renal papillary cell, kidney renal clear cell and kidney chromophore carcinomas. It suggests that the DL method gives the highest accuracy rate compared to RF and ANN for 95.15%, 91.83% and 89.22%, respectively. We believe that the results acquired in this study will make a contribution to the classification of cancer types and support doctors in their processes of decision making.</p

Analysis of transportable off-grid solar power generation for rural electricity supply: an application study of Sanliurfa, Turkey

Despite the advances in technology, electrical energy needs in rural and less developed regions are not yet fully met in terms of cost and sustainability. Nowadays, small-scale Photovoltaic (PV) systems can be transported to other regions and easily reinstalled so that these systems can be used in areas where needed for home usage and humanitarian purposes. There is no doubt that a PV-based microgrid is needed in rural and remote areas where energy is often important, and grid energy is not available or unstable. Mobility microgrid design studies can reduce time, effort, and costs significantly in such cases. Therefore, the design, modeling, and technical simulation of an isolated system based on solar energy are investigated and analyzed in this paper. This study also highlights the future trends of transportable-based isolated (off-grid) microgrid design which provides a sustainable solution for small-scale PV power generation. Additionally, an optimal solution approach for power management with Energy Storage (ES) and PV energy technologies is presented in the developed of an off-grid PV system. Aside from the designed system's cost-benefit analysis, important criteria such as lifespan, battery performance, and energy production have been evaluated. The Distributed Energy Resources (DER) with the load flow in 24-hour scenario is modeled, and simulated, also the findings are presented. Specifically, an application study for a 60.75 kWp isolated (off-grid) PV system with the 105.98 kWh ES, and 16 kVA diesel generator is discussed in terms of financial, regional, and technical parameters as well as numerical modeling, and MATLAB simulation for the province of Sanliurfa in Turkey