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This study proposes a novel ensemble empirical mode decomposition (EEMD) time-dependent intrinsic cross-correlation (TDICC)-coupled framework to investigate the correlation between monthly rainfall over India and Madden–Julian oscillation (MJO) in different timescales. EEMD first decomposes the monthly rainfall and MJO time series into different orthogonal modes namely intrinsic mode functions (IMFs) and a residue with specific periodicity representing the physical processes governing, independently. Then, the significant modes that can be used for rainfall predictions are extracted by executing time-dependent intrinsic correlation (TDIC) which follows the concept of running correlation analysis. Finally, the lags significant for rainfall predictions at different scales are identified by invoking the TDICC analysis considering different time lags up to 12 months. Among the ten MJO indices considered in the study, indices 2 to 5 (longitudes 100° E, 120° E, 140° E, 160° E) are strongly negatively correlated while MJO indices 8 to 10 (longitudes 10° W, 20° E and 70° E) are found to be strongly positively correlated with the rainfall at all the timescales. Contrary to this similarity in the nature of correlations, the correlation patterns can differ with lags both in the nature and strength of associations. The mode of rainfall at annual scale for all the indices can be predicted with lags 1, 2, 5–8 while the mode of highest frequency can be predicted with lag 1 information alone. For the prediction of the low-frequency IMFs (mostly 5th IMF onward) of all the indices, all the 12 lags are found to be significant, implied by the unchanging and stable lagged correlation pattern. [ABSTRACT FROM AUTHOR]

Forecasting of Indian summer monsoon rainfall (ISMR) is a complex problem for the hydrologists and meteorologists. The time series and data-driven methods have been used as complementary tools for forecasting ISMR against the complex physically based dynamical models due to scarcity of data and the simplicity of former approaches. The use of hybrid decomposition data–driven models is the recent improvement among the different approaches for rainfall forecasting, but these approaches differ significantly in the framework adopted. This paper presents an adaptive hybrid modelling framework so called Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN) model for forecasting ISMR, which performs the forecasts adaptively as and when new information is added. The performance of the popular EEMD-ANN hybrid hindcast and forecast experiments in the prediction of All-India SMR and southwest monsoon rainfall of the state of Kerala is compared with the proposed method. The AEEMD-ANN method achieved a predictive skill of 0.78 and 0.91, respectively for rainfall predictions for Kerala and All-India. AEEMD-ANN method performed reasonably well in capturing the hydrologic extremes when compared with EEMD-ANN forecast method, with better accuracy in capturing the drought years. The proposed method is found to be successful in capturing the extreme low SWM rainfall of year 2002 for All-India and the extreme high rainfall of Kerala 2018 with an error percentage of 1.09% and 0.52%, respectively. [ABSTRACT FROM AUTHOR]

Rainfall is a vital hydrologic variable that has a direct and significant impact on the economic development of monsoon-dominated state of Kerala in southern India. An effective approach providing accurate prediction of rainfall makes it possible to take preventive and mitigation measures against natural disasters. In this study, the ensemble empirical mode decomposition (EEMD)–artificial neural networks (ANN)–multiple linear regression (MLR) hybrid approach is used to forecast the south–west monsoon (SWM) rainfall of Kerala. The EEMD of SWM rainfall of Kerala resulted in a set of orthogonal components of specific periodic scale. The non-linear components are identified and separately modeled using ANN and rest of the components are modeled using linear regression to get their values at a specific time t. Finally, the predicted modes are recombined to get the forecasts of a generic time t. The SWM rainfalls of 1871–1972 are used for model calibration and forecasts are made sequentially for 1973–2014 period, which clearly demonstrated its efficacy in handling non-linear part of SWM rainfall data with a predictive skill of 0.65 for validation data. Further, by considering a dataset of 1961–2014 period, this study has investigated the possible teleconnection of SWM rainfall of Kerala with the Indian Ocean dipole (IOD) using the cross-correlation and EEMD-based time-dependent intrinsic correlation (TDIC) analyses. Apart from the strong correlation in the trend component, the analysis has proved the dominancy of negative association of IOD with SWM of Kerala in different process scales with strong positive association at localized time spells. The forecasting strategy demonstrated in the study and the evidence of IOD–SWM rainfall link are an amendment to the efforts for improving the predictability of SWM rainfall in Kerala. [ABSTRACT FROM AUTHOR]

Accurate prediction of rainfall is a complex problem because of the large number of controlling factors, complex interrelationships between them and the multiscaling behaviour of the process. Because of the multiscaling behaviour of the problem, the use of hybrid modelling involving decomposition techniques is preferable over strenuous physical models and standalone driven techniques for accurate rainfall predictions. This study proposes a novel hybrid modelling framework integrating Long Short Term Memory (LSTM) and Multivariate Empirical Mode Decomposition (MEMD) aided with Time Dependent Intrinsic Cross-Correlation (TDICC) analysis algorithm for monthly rainfall predictions. The application of the proposed model is demonstrated for monthly rainfall prediction of 2005–2015 period at All India spatial domain considering El-Niño Southern Oscillation(ENSO), Indian Ocean Dipole(IOD) and five antecedent values of rainfall are the input variables. The proposed framework first uses MEMD to obtain a set of orthogonal components namely Intrinsic Mode Functions (IMFs) identifying and aligning the common scales embedded in the multiple input variables considered. Subsequently, scale specific rainfall information are predicted by incorporating them on relevant IMFs and their significant lags identified through TDIC and TDICC analyses. Final aggregation of predicted rainfall components from different scales gives the monthly rainfall of a generic time step ahead. The efficacy of the proposed MEMD-TDICC-LSTM framework is compared with five other hybrid models such as MEMD-TDIC-ANN, MEMD-TDICC-ANN, MEMD-ACO-ANN, MEMD-ACO-LSTM and MEMD-TDIC-LSTM, which used Time Dependent Intrinsic Correlation (TDIC) and Ant Colony Optimization (ACO) for predictor selection and Artificial Neural Network (ANN) as modelling tool. The study has used different graphical representations and ten different statistical performance evaluation measures for the prediction of validation period, it is observed that the proposed model could achieve a predictive skill of 0.98, Nash-SutcliffeEfficiency (NSE) of 0.95 and Index of Agreement (IA) of 0.91 which is better than all the five remaining models. The capability of MEMD-TDICC-LSTM model to predict the extremes is also confirmed by using the bar graph for the drought year rainfall of 2009 and is observed that the model is successful in capturing the extremes with an annual rainfall 975.16 mm closer to observed value 927.3 mm. MEMD algorithm facilitates the inclusion of multiple large scale climatic oscillations as inputs and their multi time scale decomposition, TDICC helps to fix the relevant inputs at different time scales and the LSTM functions as the robust modelling tool. Further, the framework using this specific combination resulted in substantial reduction in modelling complexity and faster execution, as the approach considers only the most relevant and significant inputs in the process. • Novel MEMD-TDICC-LSTM framework is proposed for prediction of monthly rainfall over India. • MEMD performs robust decomposition of multiple climatic inputs and LSTM performs efficient prediction. • Presented the novel TDICC based approach for hydro-climatic teleconnection and feature selection (FS). • The proposed framework is superior to ANN hybrids and ACO-LSTM hybrids in the prediction of mean and extreme rainfall. [ABSTRACT FROM AUTHOR]

Background: One-fifth of the global population is infected with soil-transmitted helminths (STH). Mass drug administration (MDA) with deworming medication is widely implemented to control morbidity associated with STH infections. However, surveillance of human infection prevalence by collecting individual stool samples is time-consuming, costly, often stigmatized, and logistically challenging. Current methods of STH detection are poorly sensitive, particularly in low-intensity and low-prevalence populations., Methodology/principal Findings: We aimed to develop a sensitive and specific molecular method for detecting STH DNA in large volumes of soil (20 g) by conducting laboratory and proof of concept studies across field sites in Kenya, Benin, and India. We collected human stool (n = 669) and soil (n = 478) from 322 households across the three study sites. We developed protocols for DNA extraction from 20 g of soil and qPCR to detect Ascaris lumbricoides, Trichuris trichiura, Necator americanus, and Ancylostoma duodenale. Agreement between detection of STH via qPCR, digital droplet PCR (ddPCR), and microscopy-based methods was assessed using the Cohen's Kappa statistic. Finally, we estimated associations between soil characteristics and detection of STH in soil by qPCR, as well as between STH detected in soil and STH detected in stool from matched households, adjusting for soil characteristics. The overall prevalence of STH in soil by qPCR was 31% for A. lumbricoides, 3% for T. trichiura, and 13% for any hookworm species. ddPCR and qPCR performed similarly. However, there was poor agreement between STH detected in soil by qPCR versus light microscopy. Microscopy underestimated the prevalence of A. lumbricoides and N. americanus and overestimated T. trichiura. Detection of an STH species in household soil was strongly associated with increased odds of a household member being infected with that same species., Conclusions/significance: Soil surveillance for STH has several benefits over stool-based surveillance, including lower cost and higher success rates for sample collection. Considering that delivery of MDA occurs at the community level, environmental surveillance using molecular methods could be a cost-effective alternate strategy for monitoring STH in these populations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Manuel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)