Prediction of n-octanol-water partition coefficient for polychlorinated biphenyls from theoretical molecular descriptors

A quantitative structure-property relationship (QSPR) study was performed to develop models that relate the structures of 133 polychlorinated biphenyls to their n-octanol-water partition coefficients (log Kow). Molecular descriptors were derived solely from 3D structures of the molecules. The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool.  The partial least square (PLS) method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. These descriptors are: Balabane index (J), XY Shadow (SXY), Kier shape index (order 3) (3к), Wiener index (W) and Maximum valency of C atom (VmaxC). The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of log Kow for molecules not yet synthesized. The root mean square errors for ANN predicted partition coefficients of training, test and external validation sets were 0.063, 0.112 and 0.126, respectively, while these values are 0.230, 0.164 and 0.297 for the PLS model, respectively. Comparison between these values and other statistical parameters for these two models revealed the superiority of the ANN over the PLS model

Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting

Flood, which is the most common natural disaster that occurs worldwide, causes massive casualties and damages to people and environment respectively. Hence, flood prediction is integral to minimise the damage and loss of life, while simultaneously aiding the government authorities and even the private sector in making accurate decisions when faced with incoming flood. Therefore, this present study had imputed the missing hydrological data using five imputation methods, namely Neural Network (NN), Moving Median (MM), Iterative Algorithm (IA), Nonlinear Iterative Partial Least Square (NIPALS), and Combined Correlation with Inversed Distance (CCID) imputation methods. Next, a newly developed hybrid deep learning (DL) algorithm is proposed to predict the daily water level in selected rivers that flow through Kelantan. The proposed model was then compared with two benchmark models, namely single Artificial Neural Network (ANN) and Wavelet Artificial Neural Network (WANN). The outcomes revealed that the MM imputation method resulted in higher accuracy with the lowest Root Mean Square Error (RMSE) for all rainfall and streamflow stations, in comparison to the other imputation methods. The experimental results portrayed that the proposed model achieved the best prediction accuracy in all performance measurements. The Mean Arctangent Absolute Percentage Error (MAAPE) results for all rivers ranged at 1-12%, which signified higher accuracy. Essentially, the proposed model may facilitate the government authorities and private sector to predict and plan better when dealing with the occurrence of flood