Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon

Indian monsoon is an important climatic phenomenon and a global climatic marker. Both statistical and numerical prediction schemes for Indian monsoon have been widely studied in literature. Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. Meteorologists revise their models on different years based on prevailing global climatic incidents like El-Niño. These indices often have degree of severity associated with them. In this paper, we cluster the monsoon years based on their fuzzy degree of associativity to these climatic event patterns. Next, we develop individual prediction models for the year clusters. A weighted ensemble of these individual models is used to obtain the final forecast. The proposed method performs competitively with existing forecast models

Determining the best drought tolerance indices using Artificial Neural Network (ANN): Insight into application of intelligent agriculture in agronomy and plant breeding

In the present study, efficiency of the artificial neural network (ANN) method to identify the best drought tolerance indices was investigated. For this purpose, 25 durum genotypes were evaluated under rainfed and supplemental irrigation environments during two consecutive cropping seasons (2011–2013). The results of combined analysis of variance (ANOVA) revealed that year, environment, genotype and their interaction effects were significant for grain yield. Mean grain yield of the genotypes ranged from 184.93 g plot–1 under rainfed environment to 659.32 g plot–1 under irrigated environment. Based on the ANN results, yield stability index (YSI), harmonic mean (HM) and stress susceptible index (SSI) were identified as the best indices to predict drought-tolerant genotypes. However, mean productivity (MP) followed by geometric mean productivity (GMP) and HM were found to be accurate indices for screening drought tolerant genotypes. In general, our results indicated that genotypes G9, G12, G21, G23 and G24 were identified as more desirable genotypes for cultivation in drought-prone environments. Importantly, these results could provide an evidence that ANN method can play an important role in the selection of drought tolerant genotypes and also could be useful in other biological contexts

Situation recognition using soft computing techniques

Includes bibliographical references.The last decades have witnessed the emergence of a large number of devices pervasively launched into our daily lives as systems producing and collecting data from a variety of information sources to provide different services to different users via a variety of applications. These include infrastructure management, business process monitoring, crisis management and many other system-monitoring activities. Being processed in real-time, these information production/collection activities raise an interest for live performance monitoring, analysis and reporting, and call for data-mining methods in the recognition, prediction, reasoning and controlling of the performance of these systems by controlling changes in the system and/or deviations from normal operation. In recent years, soft computing methods and algorithms have been applied to data mining to identify patterns and provide new insight into data. This thesis revisits the issue of situation recognition for systems producing massive datasets by assessing the relevance of using soft computing techniques for finding hidden pattern in these systems

Meteorological Drought Forecasting Based on Climate Signals Using Artificial Neural Network – A Case Study in Khanhhoa Province Vietnam

AbstractIn Khanhhoa Province (Vietnam) long-lasting droughts often occur, causing negative consequences for this region, so accurate drought forecasting is of paramount importance. Normally, drought index forecasting model uses previously lagged observations of the index itself and rainfall as input variables. Recently, climate signals are being also used as potential predictors. In this study, we use 3-month, 6-month, and 12-month of Standardized Precipitation Evapotranspiration Index (SPEI), with a calculation time during the period from 1977 to 2014. This paper aims at examining the lagged climate signals to predict SPEI at Khanhhoa province, using artificial neural network. Climate signals indices from Indian Ocean and Pacific Ocean surrounding study area were analysed to select five predictors for the model. These were combined with local variables (lagged SPEI and rainfall) and used as input variables in 16 different models for different forecast horizons. The results show that adding climate signals can achieve better prediction. Climate signals can be also used solely as predictors without using local variables – in this case they explain the variation SPEI (longer horizons, e.g.12-month) reaching 61 – 80%. The developed model can benefit developing long-term policies for reservoir and irrigation regulation and plant alternation schemes in the context of drought hazard

Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

The reconstruction of daily precipitation data is a much-debated topic of great practical use, especially when weather stations have missing data. Missing data are particularly numerous if rain gauges are poorly maintained by their owner institutions and if they are located in inaccessible areas.In this context, an attempt was made to assess the possibility of reconstructing daily rainfall data from other climatic variables other than the rainfall itself, namely atmospheric pressure, relative humidity and prevailing wind direction.The pilot area for the study was identified in Central Italy, especially on the Adriatic side, and 119 weather stations were considered.The parameters of atmospheric pressure, humidity and prevailing wind direction were reconstructed at all weather stations on a daily basis by means of various models, in order to obtain almost continuous values rain gauge by rain gauge. The results obtained using neural networks to reconstruct daily precipitation revealed a lack of correlation for the prevailing wind direction, while correlation is significant for humidity and atmospheric pressure, although they explain only 10–20% of the total precipitation variance. At the same time, it was verified by binary logistic regression that it is certainly easier to understand when it will or will not rain without determining the amount. In this case, in fact, the model achieves an accuracy of about 80 percent in identifying rainy and non-rainy days from the aforementioned climatic parameters. In addition, the modelling was also verified on all rain gauges at the same time and this showed reliability comparable to an arithmetic average of the individual models, thus showing that the neural network model fails to prepare a model that performs better from learning even in the case of many thousands of data (over 400,000). This shows that the relationships between precipitation, relative humidity and atmospheric pressure are predominantly local in nature without being able to give rise to broader generalisations

Surrogate Optimization of Deep Neural Networks for Groundwater Predictions

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the ''simplest'' network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19 figures, 1 table; online supplement: 11 pages, 18 figures, 3 table

A Comparative Study of Deep Learning Model and Simple Prediction Charts in Construction Noise Prediction

Construction noise monitoring is crucial to assess the impacts of construction noise on the workers and surroundings. However, the existing noise prediction methods are time-consuming in which required laborious work for the computation of noise levels. This study aims to assess the accuracy and reliability of deep learning model (DL) that adopted stochastic modelling and artificial neural network (ANN) in construction noise prediction. The artificial neural network was trained with the output of stochastic modelling. The outcome of noise level prediction using simple prediction chart (SPC) and DL model was discussed and compared to 3 case studies. The case studies were conducted at construction sites located in Semenyih, Selangor, Malaysia. The results of DL model showed high accuracy of predicted noise levels along with an absolute difference of less than 2.3 dBA. Besides, the predicted noise levels are reliable as the R-squared value is higher than 0.992. On that account, DL model is proved to be reliable and accurate in noise level prediction and it has the potential to be utilized as a managerial tool to monitor construction noise more effectively