RESUMO
Water scarcity in Kurdistan-Iraq has become a crucial problem, particularly in semi-arid regions, as a result of severe droughts over the last decades. One potential solution to this water shortage is using rainwater harvesting (RWH) techniques. In this study, optimal sites of RWH in the Dewana watershed were identified using a combination of remote sensing (RS) and geographic information system (GIS), with multi-criteria decision analysis (MCDA) models, including analytical hierarchy process (AHP) and weighted sum method (WSM). Sixteen thematic layers are used. As a result of the AHP and WSM models, 236.89 km2 and 267.15 km2 were identified as highly suitable areas for RWH techniques in the suitability index map. They identified 13.06 km2 (5.55%) and 58 km2 (21.81%) as highly suitable for constructing dams in the dam site selection maps. The present study found that 11 proposed dam sites are suitable for dam construction. The weighted product model (WPM) was used to rank the proposed dam sites, with Dams #10 and #2 being the top-ranked sites. Accuracy assessment results indicated that the WSM model outperformed the AHP model with an overall accuracy rate of 50.5% and 52.78%, respectively. However, the AHP model demonstrated a higher receiver operating characteristic (ROC) and an area under the curve (AUC) score of 1.00, while the WSM model had an AUC of 0.78.
RESUMO
The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.
RESUMO
In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.
