Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms.

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

Prediction of effluent arsenic concentration of wastewater treatment plants using machine learning and kriging-based models.

This study evaluates the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In the first scenario, all of the seven independent variables were taken into account for constructing the data-driven models. For the second input scenario, the forward selection k-fold cross-validation method was employed to select effective explanatory influent parameters. The results obtained from both input scenarios show that the kriging-logistic and machine learning models are effective and robust. However, using the feature selection procedure in the second scenario not only made the architecture of the model simpler and more effective, but also enhanced the performance of the developed models (e.g., around 7.8% performance enhancement of the RMSE). Although the standard kriging method provided the least good predictive results (RMSE = 0.18 ug/l and NSE=0.75), it was revealed that the kriging-logistic method gave the best performance among the applied models (RMSE = 0.11 ug/l and NSE=0.90).

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R2), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3th distribution (NS = 0.915) and the Wavelet-SVR with 4th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD5 concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD5 concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Assessment of the total organic carbon employing the different nature-inspired approaches in the Nakdong River, South Korea.

Total organic carbon (TOC) has vital significance for measuring water quality in river streamflow. The detection of TOC can be considered as an important evaluation because of issues on human health and environmental indicators. This research utilized the novel hybrid models to improve the predictive accuracy of TOC at Andong and Changnyeong stations in the Nakdong River, South Korea. A data pre-processing approach (i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)) and evolutionary optimization algorithm (i.e., crow search algorithm (CSA)) were implemented for enhancing the accuracy and robustness of standalone models (i.e., multivariate adaptive regression spline (MARS) and M5Tree). Various water quality indicators (i.e., TOC, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), and suspended solids (SS)) were utilized for developing the standalone and hybrid models based on three input combinations (i.e., categories 1~3). The developed models were evaluated utilizing the correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe efficiency (NSE). The CEEMDAN-MARS-CSA based on category 2 (C-M-CSA2) model (CC = 0.762, RMSE = 0.570 mg/L, and NSE = 0.520) was the most accurate for predicting TOC at Andong station, whereas the CEEMDAN-MARS-CSA based on category 3 (C-M-CSA3) model (CC = 0.900, RMSE = 0.675 mg/L, and NSE = 0.680) was the best at Changnyeong station.

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters. The first scenario proposed three advanced optimisers-whale algorithm (WA), particle swarm optimization (PSO), and bat algorithm (BA)-for the optimisation of the performance of artificial neural network (ANN) in accurately predicting the suspended sediment load rate at the Goorganrood basin, Iran. In total, 5 different input combinations were examined in various lag days of up to 5 days to make a 1-day-ahead SSL prediction. Scenario 2 introduced a multi-objective (MO) optimisation algorithm that utilises the same inputs from scenario 1 as a way of determining the best combination of inputs. Results from scenario 1 revealed that high accuracy levels were achieved upon utilisation of a hybrid ANN-WA model over the ANN-BA with an RMSE value ranging from 1 to 6%. Furthermore, the ANN-WA model performed better than the ANN-PSO with an accuracy improvement value of 5-20%. Scenario 2 achieved the highest R2 when ANN-MOWA was introduced which shows that hybridisation of the multi-objective algorithm with WA and ANN model significantly improves the accuracy of ANN in predicting the daily suspended sediment load.

Assessing the biochemical oxygen demand using neural networks and ensemble tree approaches in South Korea.

The biochemical oxygen demand (BOD), one of widely utilized variables for water quality assessment, is metric for the ecological division in rivers. Since the traditional approach to predict BOD is time-consuming and inaccurate due to inconstancies in microbial multiplicity, alternative methods have been recommended for more accurate prediction of BOD. This study investigated the capability of a novel deep learning-based model, Deep Echo State Network (Deep ESN), for predicting BOD, based on various water quality variables, at Gongreung and Gyeongan stations, South Korea. The model was compared with the Extreme Learning Machine (ELM) and two ensemble tree models comprising the Gradient Boosting Regression Tree (GBRT) and Random Forests (RF). Diverse water quality variables (i.e., BOD, potential of Hydrogen (pH), electrical conductivity (EC), dissolved oxygen (DO), water temperature (WT), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P)) were utilized for developing the Deep ESN, ELM, GBRT, and RF with five input combinations (i.e., Categories 1-5). These models were evaluated by root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and correlation coefficient (R). Overall evaluations suggested that the Deep ESN5 model provided the most reliable predictions of BOD among all the models at both stations.

Advanced machine learning model for better prediction accuracy of soil temperature at different depths.

Soil temperature has a vital importance in biological, physical and chemical processes of terrestrial ecosystem and its modeling at different depths is very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four different depths. Various combinations of climatic variables are utilized as input to the developed models. The models' outcomes are also compared with multi-linear regression based on Nash-Sutcliffe efficiency, root mean square error, and coefficient of determination statistics. ELM is found to be generally performs better than the other four alternatives in estimating soil temperatures. A decrease in performance of the models is observed by an increase in soil depth. It is found that soil temperatures at three depths (5, 10 and 50 cm) could be mapped utilizing only air temperature data as input while solar radiation and wind speed information are also required for estimating soil temperature at the depth of 100 cm.

Dissolved oxygen prediction using a new ensemble method.

Prediction of dissolved oxygen which is an important water quality (WQ) parameter is crucial for aquatic managers who have responsibility for the ecosystem health's maintenance and for the management of reservoirs related to WQ. This study proposes a new ensemble method, Bayesian model averaging (BMA), for estimating hourly dissolved oxygen. The potential of the BMA was investigated and compared with five data-driven methods, extreme leaning machine (ELM), artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), classification and regression tree (CART), and multilinear regression (MLR), by considering hourly temperature, pH, and specific conductivity data as inputs. The methods were compared with respect to three statistics, root mean square errors (RMSE), Nash-Sutcliffe efficiency, and determination coefficient. Results based on two stations' data indicated that the proposed method performed superior to the ELM, ANN, ANFIS, CART, and MLR in estimation of hourly dissolved oxygen; corresponding improvements obtained by BMA are about 5-8%, 13-12%, 7-9%, and 18-27% with respect to RMSE. The ELM also outperformed the other four methods (ANN, ANFIS, CART, and MLR), and the CART and MLR indicated the lowest estimation accuracy in both stations. Examination of various input combinations revealed that the most effective variable is water temperature while the specific conductivity has negligible effect on hourly dissolved oxygen.