•Using Multilayer Perceptron Artificial Neural Network (MLP-ANN) model can effectively fill the GRACE/GRACE-FO data gap.•Modified Flood Potential Index (MFPI) is shown to be reliable for flood events ...detection and spatiotemporal processes monitoring.•GRACE-based MFPI outperforms other commonly used flood indexes in terrestrial water storage depletion basin.
The Gravity Recovery and Climate Experiment (GRACE) and Follow-On (GRACE-FO) missions offer great potential for large-scale flood event monitoring. However, the effectiveness of current GRACE-based indices may be limited in areas with significant Terrestrial Water Storage (TWS) decline. In this study, a Modified Flood Potential Index (MFPI) was proposed to improve the monitoring capability for flood events in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB). First, the GRACE/FO Terrestrial Water Storage Anomaly (TWSA) data gap was filled with a Multilayer Perceptron Artificial Neural Network Model (MLP-ANN). Based on the gap-filled data, the performance of the MFPI was compared with five selected indices: Flood Potential Index (FPI), Total Storage Deficit Index (TSDI), Modified Total Storage Deficit Index (MTSDI), Water Storage Deficit Index (WSDI), and Combined Climatologic Deviation Index (CCDI). Finally, the spatiotemporal monitoring ability of the MFPI was evaluated for typical and atypical flood events. We found that (1) MLP-ANN was able to predict the GRACE/FO data gap with a performance of ‘Very Good’; (2) the MFPI outperformed the FPI, TSDI, MTSDI, WSDI, and CCDI in capturing flood events, and could also reflect their spatiotemporal processes; and (3) the MFPI avoids long-term TWSA trend effects on flood event monitoring and is available for areas with significant TWSA trends. This study can provide a reference for using GRACE data to monitor large-scale flood events.
This paper develops a Multilayer Perceptron (MLP) smoothness detector for the hybrid WENO scheme. Since the MLP detector contains nonlinear activation functions and large matrix operators, we analyze ...and reduce it to a simplified MLP (SMLP) detector for efficiency. In the hybrid WENO scheme, both detectors can be adopted to identify whether the reconstruction stencil is a smooth region or not. To improve the spectral resolution of the hybrid scheme, a high-frequency region is introduced. Thus, the high order linear reconstruction, WENO type reconstruction, and blending reconstruction are performed on the smooth, non-smooth, and high-frequency regions. Numerical tests and comparisons for Euler equations are presented to demonstrate the robustness and performance of the hybrid scheme and the efficiency of the simplified MLP detector.
•Less dissipative and dispersive errors.•Robust essentially non-oscillatory property for strong shock waves.•Good performance for the computation of small scale structures.•The efficiency and effectiveness of the simplified MLP detector.
A two-step methodology was developed to forecast tropospheric ozone (O3) concentration levels, k hours ahead (k = 1, 8, 12, 24), combining meteorological, air quality and industrial emissions data, ...across three air quality monitoring stations in Sines Portuguese region. Firstly, the best O3 concentration predictors have been identified through Classification and Regression Trees techniques; then Multilayer Perceptron models were adopted to forecast O3 levels for each monitoring site.
The obtained generalization model performances are very good to classify in advance the expected class of O3 concentration level. Performance results vary from 70% of success to forecast O3 class above 70 μg/m3 24 h in advance up to 99% to predict the next hour in advance. These successful results are favorable to be implemented in a real-time tool for health and environmental advisories, allowing the forecast of air pollutants concentrations up to 24 h ahead, improving the local air quality management systems.
•A two-step framework based on CART techniques and MLP models to forecast O3 levels.•Industrial emissions and meteorological factors stand as best pollutant predictors.•MLP models showed very good predictive success up to 24 h in advance.•Successful results promising to establish a public-health oriented Space-Time forecast tool.
This study aimed to model the relationships between input and output energies of tobacco production in northern Iran, Guilan Province, and investigate how farm size would affect the energy use ...indices in the agro-system. A multilayer perceptron (MLP) neural network was designed to forecast the energy output in the tobacco agro-system. To analyze the effect of farm size, tobacco farms were divided into three groups of small-sized (˂0.5 ha), medium-sized (0.5–1 ha), and large-sized farms (˃1 ha). The findings highlighted that the total input energy and the energy use efficiency of tobacco production agro-system were 172,831.65 MJ ha
−1
and 0.009, respectively. Natural gas used in the initial curing of green tobacco leaves accounted for around 60% of total input energy in the tobacco agro-system. Larger farms were significantly superior to the small-sized and medium-sized ones in energy use indicators (
p
˂ 0.05). The most appropriate model to estimate the output energy of tobacco production was found to have a topology of 8-20-1. It can be concluded that the proposed MLP model is a robust tool to estimate the output energy of tobacco production in northern Iran.
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Friction stir welding (FSW) is a solid-state joining technique where the joint strength is mainly influenced by three process parameters, namely, spindle speed (N), welding speed (V), and plunge ...force (Fz). The modelling of complex relationships between the process parameters and joint strength requires many experiments, which is a challenging, time-consuming, and non-economical affair. To tackle this problem, computational mathematical models such as deep learning (DL) can be employed to predict the joint strength reliably. In this paper, DL techniques, namely, deep multilayer perceptron (DMLP) and long short-term memory (LSTM) networks have been proposed for such a purpose. The DL networks were first trained with the FSW experimental data and then, the pre-trained models were used for predicting the weld strength. It was found that the DMLP and LSTM models provided lower prediction errors, which are RMSE of 3.30 and 7.63, respectively, and can be effectively utilized for determining weld quality. The proposed DL-based techniques were further compared with the traditional models - the shallow artificial neural network (SANN) model having an RMSE of 27.11 and the ANFIS model having an RMSE of 5.31. DMLP was found to be superior in determining the weld strength most accurately.
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•Chlorophyll is important in assessing algae growth and producing nutraceuticals.•LR and ANN techniques are used to predict chlorophyll content in microalgae.•RMSE, prediction ...accuracy and R2 metrics are used as evaluation benchmarks.•Both prediction models are superior to conventional spectrophotometry method.•ANN with low RMSE is an efficient chlorophyll concentration prediction model.
This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red–greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.
The two main challenges of predicting the wind speed depend on various atmospheric factors and random variables. This paper explores the possibility of developing a wind speed prediction model using ...different Artificial Neural Networks (ANNs) and Categorical Regression empirical model which could be used to estimate the wind speed in Coimbatore, Tamil Nadu, India using SPSS software. The proposed Neural Network models are tested on real time wind data and enhanced with statistical capabilities. The objective is to predict accurate wind speed and to perform better in terms of minimization of errors using Multi Layer Perception Neural Network (MLPNN), Radial Basis Function Neural Network (RBFNN) and Categorical Regression (CATREG). Results from the paper have shown good agreement between the estimated and measured values of wind speed. According to the result, it can be concluded that ANN model with MLPNN could produce the acceptable prediction of the wind speed for given on wind direction.
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•To predict the wind speed (m/sec) at 65 m height.•To build the ANN Models and Categorical Regression (CATREG) for wind speed prediction.•To produce the validated and acceptable prediction.
Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for ...environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM 2 . 5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM 2 . 5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.
The most acceptable method to estimate tsunami inundation caused by a submarine earthquake is by conducting a nonlinear tsunami simulation. However, this method has the disadvantages of a relatively ...high computational cost and the necessity for immediate warning announcements when a tsunami is imminent. In this study, to overcome this problem, we apply two machine learning models, a convolutional neural network and a multilayer perceptron, to estimate tsunami inundation in real time. We run multiple fault scenarios and store the result of the maximum tsunami amplitude in a low-resolution grid and the associated tsunami inundation in a high-resolution grid in the database. The convolutional neural network selects tsunami inundation in the high-resolution grid as the forecast based on pattern similarity between the input, which is the results of linear forward modeling in the low-resolution grid, and the precomputed patterns in the database. Slightly different from the convolutional neural network, instead of selecting the best-fit scenario in the database, the multilayer perceptron directly generates the inundation forecast based on knowledge acquired during the training process. We conduct an experiment using the hypothetical future Nankai megathrust earthquake with Atashika and Owase Bays in Japan as the study cases. The results show that our proposed methods are extremely fast (less than 1 s) and comparable with nonlinear forward modeling. Therefore, the proposed methods can be used as a deterministic model for real-time simulation.
•GS/SVR approach is a good predictive model of the Chl-a in reservoirs.•The correlation coefficient of the GS/SVR - relied model is about 0.9.•The relevance of physico-chemical input variables is ...established.•This approach for Chl-a is transferable to other in-lake model applications.•Ecosystem understanding must combine the modelling with samplings in-situ.
The trophic condition of bodies of water, such as oceans, lakes, and reservoirs, can be accurately assessed thanks to the use of chlorophyll-a, or Chl-a, as an indicator of phytoplankton biomass and abundance. In fact, the main molecule in charge of photosynthesis is Chl-a. This work presents a powerful and reliable nonparametric method for predicting the concentration of Chl-a in El Val reservoir using a dataset containing 240,765 samples: the Support Vector Regression (SVR) with different kinds of kernels. This mathematical SVR-relied model was constructed using five years (2018–2022) of water quality variable monitoring (physico-chemical independent variables) in the El Val reservoir (located in the northeast of Spain). For comparison, M5 model trees, a the Multilayer Perceptron (MLP), that is a particular type of artificial neural network (ANN), and multivariate linear regression (MLR) were also used for the same observed data. The Grid Search (GS) algorithm was employed as an optimizer; this approach greatly improves the regression precision by allowing the optimal kernel parameters to be chosen during the SVR training phase. There are two ways to sum up the findings of this investigation. First, it is determined how relevant each input variable is to the Chl-a concentration in the El Val reservoir. Second, this hybrid GS/SVR-relied model with PUK kernel can accurately predict the Chl-a (R2 and r values were 0.8989 and 0.9499, respectively). The model’s agreement with the observed data amply proves the remarkable efficacy of this creative strategy.
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