Stacking-based uncertainty modelling of statistical and machine learning methods for residential property valuation

Estimating real estate prices helps to adapt informed policies to regulate the real estate market and assist sellers and buyers to have a fair business. This study aims to estimate the price of residential properties in District 5 of Tehran, Capital of Iran, and model its associated uncertainty. The study implements the Stacking technique to model uncertainties by integrating the outputs of basic models. Basic models must have a good performance for their combinations to have acceptable results. This study employs four statistical and machine learning models as basic models: Random Forest (RF), Ordinary Least Squares (OLS), Weighted K-Nearest Neighbour (WKNN), and Support Vector Regression (SVR) to estimate the price of residential properties. The results show that the integrated output is more accurate for the quadruple combination mode than for any of the binary and triple combinations of the basic models. Comparing the Stacking technique with the Voting technique, it is shown that the Mean Absolute Percentage Error (MAPE) reduces from 10.18% to 9.81%. Hence we conclude that our method performs better than the Voting technique.</p

PLAY-BASED HYDROCARBON EXPLORATION UNDER SPATIAL UNCERTAINTY USING EVIDENTIAL THEORY

Hydrocarbon exploration is a process based on the prediction of existing hydrocarbon in the underground formations which is associated with uncertainties. A number of studies have been undertaken on the extent of these uncertainties in the risk maps concerned with hydrocarbon exploration. This paper has addressed this issue using a novel approach. The differences of the proposed method are checked in a few cases. Firstly, the level of studying the hydrocarbon system is play which refers to an area with a potential for trapping hydrocarbon with a unique petroleum system. Second, the evidential theory was used to accurately examine the uncertainty in the maps of the hydrocarbon system. Finally, the model used to produce the final risk map is developed in a geospatial information system environment. The results of the research show that the functions proposed in the model are accurately estimated the uncertainty in the prediction of the existence of hydrocarbon systems in the study area. The CCRS map outlines approximately 25.9&thinsp;% of the study area which is highly promising for the hydrocarbon potential reservation. According to the obtained results, around 61.2&thinsp;% of the prospects have low risk of hydrocarbon potential in the area having high belief and about 43.7&thinsp;% of the prospects are available with high risk of hydrocarbon potential in the regions with high uncertainty.</p

Attacking RO-PUFs with Enhanced Challenge-Response Pairs

This paper studies the security of Ring Oscillator Physically Unclonable Function (PUF) with Enhanced Challenge-Response Pairs as proposed by Delavar et al. We present an attack that can predict all PUF responses after querying the PUF with n+2 attacker-chosen queries. This result renders the proposed RO-PUF with Enhanced Challenge-Response Pairs inapt for most typical PUF use cases, including but not limited to all cases where an attacker has query access

TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 &mu;m or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 &plusmn; 9.8 &mu;g/m3) and predicted data (56.6 &plusmn; 13.3 &mu;g/m3) compared with those of the IDW in the test (47.7 &plusmn; 22.43 &mu;g/m3) and predicted set (62.18 &plusmn; 26.1 &mu;g/m3). However, in PIs, IDW ([0, 0.7] &mu;g/m3) compared with the OK ([0, 0.5] &mu;g/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 &mu;g/m3 and a standard deviation of 9.8 &mu;g/m3 and PIs between [2.7 &plusmn; 4.8, 14.9 &plusmn; 15] &mu;g/m3

GROUNDWATER LEVEL PREDICTION USING DEEP RECURRENT NEURAL NETWORKS AND UNCERTAINTY ASSESSMENT

Groundwater is one of the most important sources of regional water supply for humans. In recent years, several factors have contributed to a significant decline in groundwater levels (GWL) in certain regions. As a result of climate change, such as temperature increase, rainfall decrease, and changes in relative humidity, it is necessary to investigate and model the effects of these factors on GWL. Although a number of researches have been conducted on GWL modeling with machine learning (ML) and deep learning (DL) algorithms, only a limited number of studies have reported model uncertainty. In this paper, GWL modeling of some piezometric wells has been conducted by considering the effects of the meteorological parameters with Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The models were trained on one piezometric well data and predictions were executed on six other wells. To perform an uncertainty assessment, the models were run 10 times and their means were calculated. Subsequently, their standard deviations were considered to evaluate the outcomes. In addition, the prediction power of the models was validated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and R-Squared (R2). Finally, for all the six wells that did not participate in the training phase, the prediction functions of the trained models were run 10 times and their accuracy was assessed. The results indicate that LSTM (R2=95.6895, RMSE=0.4744 m, NRMSE=0.0558, MAE=0.3383 m) had a better performance compared to that of GRU (R2=95.2433, RMSE=0.4984 m, NRMSE=0.0586, MAE=0.3658 m) on the GWL modeling