Stacked Hybridization to Enhance the Performance of Artificial Neural Networks (ANN) for Prediction of Water Quality Index in the Bagh River Basin, India

Data availability statement: The data pertaining to this study have not been deposited in a publicly accessible repository, given that all relevant data are thoroughly detailed in the article or appropriately cited in the manuscript.Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.No funding was received for conducting this study

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Not AvailableBand ratios are one of the most common features for discrimination and identification of minerals using remotely sensed data. The band ratios to be used are determined mainly by spectral analysis and trial experiments. In the present work, we systematically examine the best features and best band ratios using Bhattacharyya distance based optimal band analysis. The experiments indicate that the ASTER nine bands and band ratios within the wavelength range of 2200 nm to 2300 nm are significant for hydrothermal alteration minerals.Not Availabl

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Not AvailableNatural disasters like floods are becoming more and more devastating every year due to increase in rainfalls and other factors induced by climate changes. The losses due to flood can be greatly minimized by the effective early detection systems. There are many traditional wireless sensor network methods currently available for this. But this paper gives a detailed study of how the current trending field of information technology called internet of things is applied for an efficient implementation of the early warning flood detection systems. The paper describes how the flood can be predicted by extracting various parameters from the environment that contributes to the flood. A fully connected feed forward artificial neural network is used here for the prediction purpose for giving early warning and communicating it to the target users. In the experiment, an Internet of Things platform, Thingspeak is used for real-time visualization of the sensor data. The alerts are sent to the preconfigured email IDs and mobile numbers of the authorities and the communities without any delay.Not Availabl

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Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress

A novel approach in lactose based induction for enhanced production of 1-4-beta xylanase by recombinant Escherichia coli

Escherichia coli BL21 (DE3) with a plasmid vector pET-22b (+) carrying xylanase coding gene isolated from an extremely thermophilic bacterium, Thermotoga sp. was used in this work for enhanced production of recombinant xylanase. The study was focused on optimization of lactose based induction for enhanced production of xylanase. Data collected from OFAT and DoE based trails indicated that initiation of lactose based induction at the very early phase of cell growth in maltose base media enhances xylanase production significantly. During further induction strategy optimization trails, intracellular xylanase production was enhanced up to 2600 IU mL-1 when the expression was induced by 8 gL-1 of lactose at early log phase (between 2-4 hours) of the cell growth. From induction temperature optimization trials, it was found that xylanase production can be further enhanced by reducing the incubation temperature from 37 to 32ºC. Using the optimum induction strategy developed, up to 6430 IU mL-1 xylanase activity was achieved. This value was almost 3 folds higher than those obtained in IPTG induced cultures