Correlation approach in predictor selection for groundwater level forecasting in areas threatened by water deficits

Reliable long-term groundwater level (GWL) prediction is essential to assess the availability of resources and the risk to drinking water supply in changing climatic and socio-economic conditions, especially in areas with water deficits. The modern approach in this area involves the use of machine learning methods. However, the greatest challenge in these methods lies in the optimization of input selection. The presented research concerns the selection of the best combination of predictors using the Hellwig method. It served as a preprocessing technique before GWL prediction using support vector regression (SVR) and multilayer perceptron (MLP) for three wells in the Greater Poland Province, where the largest water deficits occur, in the period 1975–2014. The results of this method were compared with those of the regression method, general regression model. For the case study under investigation, the Hellwig method found GWL at lags of −1 and −2 months, all precipitation from the current month, and delayed by −1 to −6 months, and past temperature at months −1, −3, −4 and −6 as the most informative input set. Such input led to a model accuracy of 0.003–0.022 for a mean squared error and r2 of >0.8. The results obtained with SVR were slightly better than those with MLP. Moreover, every well required an individual set of predictors, and additional meteorological inputs improved the models’ performance.

Development of Dissolved Oxygen Forecast Model Using Hybrid Machine Learning Algorithm with Hydro-Meteorological Variables

The ability to predict dissolved oxygen, which is a critical water quality (WQ) parameter, is critical for aquatic managers responsible for maintaining ecosystem health and the management of reservoirs affected by WQ. This paper reports forecasting dissolved oxygen (DO) concentration using multivariate adaptive regression splines (MARS) of running river water using a set of water quality and hydro-meteorological variables. This study’s key objectives were to assess input selection methods and five multi-resolution analyses as a data extraction approach. Moreover, the hybrid model is prepared by maximum overlap discrete wavelet transformation (MODWT) with the MARS model (i.e., MODWT-MARS). The proposed model is further compared with numerous machine learning methods. The result shows that the hybrid algorithms (i.e., MODWT-MARS) outperformed the other models (r = 0.981, WI = 0.990, RMAE = 2.47% and MAE = 0.089). This hybrid method may serve as the foundation for forecasting water quality variables with fewer predictor variables.

SOSIALISASI APLIKASI HEC (Hybrid Evaporative Cooler) DI SMK PGRI CIKAMPEK

ABSTRAK Aplikasi HEC atau yang disebut dengan Hybrid Evaporate Cooler, aplikasi ini dibuat menggunakan pengembangan operasi pada aplikasi android berbasis web yaitu MIT APP INVENTOR. Aplikasi yang diinstal pada smartphone/android untuk mengontrol alat HEC. Sosialisasi ini dilaksanakan di SMK PGRI Cikampek khususnya siswa jurusan RPL (Rekayasa Perangkat Lunak) sehingga siswa bisa mengetahui software MIT APP INVERTOR dan siswa diharapkan bisa membuat aplikasi secara mandiri. Setelah Aplikasi diinstal di android, siswa bisa menggunakan menu yang ada pada aplikasi HEC diantaranya adalah tombol go to screen 3, tombol back to screen 1, memilih perangkat bluetooth, menampilkan data kelembapan, pengiriman data ke database dengan media firebase web, input melalui suara, pemilihan input kecepatan ON/OFF. Siswa bisa memilih tombol menu yang diinginkan sesuai dengan menu yang ada pada aplikasi. Software MIT APP INVENTOR bisa terhubung dengan alat HEC dengan menyalakan Bluetooth pada android terlebih dahulu. Selain meng-edukasi secara teknologi, kami juga memberi memberikan pengetahuan bagaimana menginstall dan penggunaan aplikasi HEC pada android yaitu menggunakan software MIT APP Inventor. Kata-kata kunci: HEC (hybrid evaporative cooler); MIT APP inventor; bluetooth. ABSTRACTThe HEC application or what is called the Hybrid Evaporate Cooler, this application was created using the development of operations on a web-based android application, namely MIT APP INVENTOR. Application installed on smartphone/android to control HEC tools. This socialization was carried out at SMK PGRI Cikampek, especially students majoring in RPL (Software Engineering) so that students could know the MIT APP INVERTOR software and students were expected to be able to make applications independently. After the application is installed on android, students can use the menus in the HEC application including the go to screen 3 button, the back to screen 1 button, selecting a bluetooth device, displaying humidity data, sending data to the database using firebase web media, input via voice, ON/OFF speed input selection. Students can choose the desired menu button according to the menu in the application. The MIT APP INVENTOR software can connect with HEC devices by turning on Bluetooth on Android first. In addition to educating technology, we also provide knowledge on how to install and use the HEC application on Android, using the MIT APP Inventor software. Keywords: HEC (hybrid evaporative cooler); MIT APP inventor; bluetooth.

Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation

Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction in effectiveness of drilling. Therefore, it boosts the total cost of mining operations. This investigation intends to develop optimized support vector machine models to forecast back-break caused by blasting. The Support Vector Machine (SVM) model was optimized using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) and moth–flame optimization (MFO). Before the models’ development, an evolutionary random forest (ERF) technique was used for input selection. This model selected five inputs out of 10 candidate inputs to be used to predict the back break. These two optimized SVM models were evaluated using various performance criteria. The performance of these two models was also compared with other hybridized SVM models. In addition, a sensitivity evaluation was made to find how the selected inputs influence the back-break magnitude. The outcomes of this study demonstrated that both the SVM–MFO and SVM–WOA improved the performance of the standard SVM. Additionally, the SVM–MFO showed a better performance than the SVM–WOA and other hybridized SVM models. The outcomes of this research recommend that the SVM–MFO can be considered as a powerful model to forecast the back-break induced by blasting.

Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique

In geotechnical engineering, there is a need to propose a practical, reliable and accurate way for the estimation of pile bearing capacity. A direct measure of this parameter is difficult and expensive to achieve on-site, and needs a series of machine settings. This study aims to introduce a process for selecting the most important parameters in the area of pile capacity and to propose several tree-based techniques for forecasting the pile bearing capacity, all of which are fully intelligent. In terms of the first objective, pile length, hammer drop height, pile diameter, hammer weight, and N values of the standard penetration test were selected as the most important factors for estimating pile capacity. These were then used as model inputs in different tree-based techniques, i.e., decision tree (DT), random forest (RF), and gradient boosted tree (GBT) in order to predict pile friction bearing capacity. This was implemented with the help of 130 High Strain Dynamic Load tests which were conducted in the Kepong area, Malaysia. The developed tree-based models were assessed using various statistical indices and the best performance with the lowest system error was obtained by the GBT technique. The coefficient of determination (R2) values of 0.901 and 0.816 for the train and test parts of the GBT model, respectively, showed the power and capability of this tree-based model in estimating pile friction bearing capacity. The GBT model and the input selection process proposed in this research can be introduced as a new, powerful, and practical methodology to predict pile capacity in real projects.

Ethical machine decisions and the input-selection problem

This article is about the role of factual uncertainty for moral decision-making as it concerns the ethics of machine decision-making (i.e., decisions by AI systems, such as autonomous vehicles, autonomous robots, or decision support systems). The view that is defended here is that factual uncertainties require a normative evaluation and that ethics of machine decision faces a triple-edged problem, which concerns what a machine ought to do, given its technical constraints, what decisional uncertainty is acceptable, and what trade-offs are acceptable to decrease the decisional uncertainty.

Research on Input Scheme Selection of a Novel Parallel Mechanism

When the two arms of the robot are transporting the heavy loads together, a new parallel mechanism is formed. The actuator input selection and optimization of the parallel mechanism are basic and important problems in mechanism research. In this paper, a 2-RPPPS dual-arm robot is taken as the research object. Firstly, based on the screw theory and input selection principle, 158 reasonable schemes are obtained. Then, an evaluation mechanism is established to screen out the schemes that do not conform to the input selection principle. Then, the end effector of the parallel mechanism moves along two different trajectories. Using the particle swarm optimization algorithm, the inverse kinematics solution of each trajectory is obtained, and the velocity and acceleration of each actuator under different trajectories are obtained. Finally, the motion stability of each actuator is evaluated, and the best scheme is selected. The results show that the best input scheme can be selected according to different trajectories, so as to improve the performance of the parallel mechanism. To the authors’ knowledge, no one has done any research on selecting the appropriate input scheme according to the trajectory of the end effector.

Comparing Deep and Shallow Neural Networks in Forecasting Call Center Arrivals

Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Deep Learning method known as Echo State Networks, is compared with a completely different shallow Neural Networks strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals similar predictive performance, although the shallow approach seems to be more robust and less demanding in terms of time-to-predict.