Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation

In construction project management, there are several factors influencing the final project cost. Among various approaches, estimate at completion (EAC) is an essential approach utilized for final project estimation. The main merit of EAC is including the probability of the project performance and risk. In addition, EAC is extremely helpful for project managers to define and determine the critical throughout the project progress and determine the appropriate solutions to these problems. In this research, a relatively new intelligent model called deep neural network (DNN) is proposed to calculate the EAC. The proposed DNN model is authenticated against one of the predominated intelligent models conducted on the EAC prediction, namely, support vector regression model (SVR). In order to demonstrate the capability of the model in the engineering applications, historical project information obtained from fifteen projects in Iraq region is inspected in this research. The second phase of this research is about the integration of two input algorithms hybridized with the proposed and the comparable predictive intelligent models. These input optimization algorithms are genetic algorithm (GA) and brute force algorithm (BF). The aim of integrating these input optimization algorithms is to approximate the input attributes and investigate the highly influenced factors on the calculation of EAC. Overall, the enthusiasm of this study is to provide a robust intelligent model that estimates the project cost accurately over the traditional methods. Also, the second aim is to introduce a reliable methodology that can provide efficient and effective project cost control. The proposed GA-DNN is demonstrated as a reliable and robust intelligence model for EAC calculation.

Analisis Optimasi Input Produksi Jagung di Desa Waimangurah, Kecamatan Wewewa Barat, Kabupaten Sumba Barat Daya

Analysis on Input Optimization of Corn Production in Waimangura Village, Sub-District of West Wewewa, Southwest Sumba RegencyThe purposes of the study were: to determine the influence of factors of productionin corn farming on production and to analyze the level optimum use of productioninputs in the corn farming. The research area was determined purposively. Theanalytical methods used to analyze the production inputs that affect the productionwere a Cobb-Douglass model of Production Function, and marginal product valueanalysis to determine the level of optimization of the use of production inputs. Theresults of the study are as follows: (1) Production inputs used in the corn farming arethe area of land, seed, labor, Urea, NPK fertilizer, M-8 fertilizer, Super ACI fertilizerand fungicides. (2) The use of production inputs in the study area are as follows,Partially, only urea fertilizer significantly affected the corn production, while otherproduction inputs were not significantly affected on the corn farming. (3) The use ofproduction inputs in the study area have not been optimal. For the use of productioninputs of urea, M-8 and Super ACI fertilizers was not optimal. Thus, the use ofproduction inputs need to be added to reach optimal use of production inputs, whileland, labor, seed, NPK fertilizer and fungicides have exceeded the optimal use so thatthe use of inputs should be reduced to gain optimal results.