Application of Hybrid ANN Techniques for Drought Forecasting in the Semi-Arid Region of India.

The intensity and frequency of diverse hydro-meteorological disasters viz., extreme droughts, severe floods, and cyclones have increasing trends due to unsustainable management of land and water resources, coupled with increasing industrialization, urbanization and climate change. This study focuses on the forecasting of drought using selected Artificial Neural Network (ANN)-based models to enable decision-makers to improve regional water management plans and disaster mitigation/reduction plans. Four ANN models were developed in this study, viz., one conventional ANN model and three hybrid ANN models: (a) Wavelet based-ANN (WANN), (b) Bootstrap based-ANN (BANN), and (c) Wavelet-Bootstrap based-ANN (WBANN). The Standardized Precipitation Evapotranspiration Index (SPEI), the best drought index identified for the study area, was used as a variable for drought forecasting. Three drought indices, such as SPEI-3, SPEI-6 and SPEI-12 respectively representing "short-term", "intermediate-term", and "long-term" drought conditions, were forecasted for 1-month to 3-month lead times for six weather stations over the study area. Both statistical and graphical indicators were considered to assess the performance of the developed models. For the hybrid wavelet model, the performance was evaluated for different vanishing moments of Daubechies wavelets and decomposition levels. The best-performing bootstrap-based model was further used for analysing the uncertainty associated with different drought forecasts. Among the models developed for drought forecasting for 1 to 3 months, the performances of the WANN and WBANN models are superior to the simple ANN and BANN models for the SPEI-3, SPEI-6, and SPEI-12 up to the 3-month lead time. The performance of the WANN and WBANN models is the best for SPEI-12 (MAE = 0.091-0.347, NSE = 0.873-0.982) followed by SPEI-6 (MAE = 0.258-0.593; NSE = 0.487-0.848) and SPEI-3 (MAE = 0.332-0.787, NSE = 0.196-0.825) for all the stations up to 3-month lead time. This finding is supported by the WBANN analyze uncertainties as narrower band width for SPEI-12 (0.240-0.898) as compared to SPEI-6 (0.402-1.62) and SPEI-3 (0.474-2.304). Therefore, the WBANN model is recommended for the early warning of drought events as it facilitates the uncertainty analysis of drought forecasting results.

Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials.

This work presents a soft-sensor approach for estimating critical mechanical properties of sandcrete materials. Feed-forward (FF) artificial neural network (ANN) models are employed for building soft-sensors able to predict the 28-day compressive strength and the modulus of elasticity of sandcrete materials. To this end, a new normalization technique for the pre-processing of data is proposed. The comparison of the derived results with the available experimental data demonstrates the capability of FF ANNs to predict with pinpoint accuracy the mechanical properties of sandcrete materials. Furthermore, the proposed normalization technique has been proven effective and robust compared to other normalization techniques available in the literature.

Performance evaluation of hybrid constructed wetlands for nitrogen removal and statistical approaches.

Nitrogen pollution in water bodies has become a pressing environmental and public health issue worldwide, demanding the implementation of effective nitrogen removal strategies. This research paper delves into the performance evaluation of hybrid constructed wetlands (HCWs) as a sustainable and innovative approach for nitrogen removal, employing a comprehensive year-long dataset gathered from a practical setup. The study collected data under diverse operating conditions to investigate the effectiveness of HCWs in removing nitrogen. Results revealed that HCWs achieved nitrogen removal efficiencies ranging from 28% to 65%, influenced by temperature and hydraulic retention time. Optimal removal occurred at an average temperature of 28°C and a 4-day hydraulic retention time. Notably, performance declined during colder periods, with temperatures below 15°C. The study also aims to predict nitrogen removal by three modeling techniques, that is, artificial neural networks (ANNs), support vector machines Pearson VII kernel function (SVM PUK), and multiple linear regression (MLR). Prediction has been done considering temperature (TEMP), hydraulic loading rate (HLR), initial concentration of chemical oxygen demand (COD) (CODin), initial concentration of total nitrogen (TNin ), initial concentration of total phosphorous (TPin ), and initial concentration of turbidity (TBin ) as input parameters, whereas reduction of total nitrogen (RED TN) is regarded as output parameter. The performance of the soft computing techniques has been compared in terms of coefficient of determination (R2 ), root mean square error (RMSE), and mean absolute error (MAE). The analysis revealed that the performance of the SVM (PUK) model (R2 : 0.572, RMSE: 0.0359, MAE: 0.0294) for the prediction of TN reduction is superior followed by MLR (R2 : 0.562, RMSE: 0.0365, MAE: 0.0294) and ANN (R2 : 0.597, RMSE: 0.0377, MAE: 0.0301). The present study concludes that the treated effluent by the HCWs, using water hyacinth and water lettuce, is of fair quality, thus having potential application for the treatment of rice mill wastewater in warmer climates. Further, machine learning approaches employed in estimating the total nitrogen reduction by HCWs technology have shown promising applicability and utilization in such studies. PRACTITIONER POINTS: Hybrid constructed wetlands (HCWs) are effective in removing nitrogen from wastewater. The performance of HCWs in nitrogen removal can vary due to physical, chemical, and biological processes. The performance of the HCWs highly depends on temperature and hydraulic retention time. Artificial neural networks (ANNs) and support vector machines (SVMs) provided better predictions of nitrogen removal with high accuracy and low root mean square error.

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete.

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

Evidence of the correlation between a city's air pollution and human health through soft computing.

Huge quantities of pollutants are released into the atmosphere of many cities every day. These emissions, due to physicochemical conditions, can interact with each other, resulting in additional pollutants such as ozone. The resulting accumulation of pollutants can be dangerous for human health. To date, urban pollution is recognized as one of the main environmental risk factors. This research aims to correlate, through soft computing techniques, namely Artificial Neural Networks and Genetic Programming, the data of the tumours recorded by the Local Health Authority of the city of Benevento, in Italy, with those of the pollutants detected in the air monitoring stations. Such stations can monitor many pollutants, i.e. NO2, CO, PM10, PM2.5, O3 and Benzene (C6H6). Assuming possible effects on human health in the medium term, in this work we treat the data relating to pollutants from the 2012-2014 period while, the tumour data, provided by local hospitals, refer to the time interval 2016-2018. The results show a high correlation between the cases of lung tumours and the exceedance of atmospheric particulate matter and ozone. The explicit genetic programming knowledge representation allows also to measure the relevance of each considered pollutant on human health, evidencing the major role of PM10, NO2 and O3.

Supervise the physicochemical quality of ground water using soft computing technique.

Water resources are essential for human beings and nowadays polluted water jeopardizes the human health. Toxic substances come from houses, industries and farm lands, dust mix with water causes water pollution. This pollution depreciates the quality of water and affects the human life. In this paper, our objective is to evaluate and supervise the physicochemical quality of the ground water, for the safety of human beings. The sample quality of 15 sites was used for measuring important parameters like pH, EC, Ca2+, Mg2+, Na+, K+, Cl-, SO42-, Also, NH4+ and NO3-, Fe2+ and HCO3-12 were considered for performance analysis. A soft computing component fuzzy logic system is used to design an intelligent system. The fuzzy logic system-based model measures groundwater quality status along with its sustainability. The results obtained from the model help the authorities, policy makers to plan proper policies for geochemical operation (water treatment process) and a foundation for observing the physicochemical quality of water in the area.