Vietnam is prone to tropical storms. Climate change effects contribute to sea level rise, floods, progression of the low water line and coastal erosion. This paper inventories the perception of local people, assesses and values main aspects of the livelihood damage caused by the tropical storms of the period 2008–2013 in three coastal communes of the Ky Anh District of the Ha Tinh Province in Central Vietnam. The communes were selected because the location of their coastal line is perpendicular to the storm itself, which made them prone to damage. The effects of increasingly extreme weather conditions on three communities in an area most affected by storms and floods on the local residents and their responses to these changing environmental conditions are analyzed and assessed. The results of questionnaires completed by randomly selected local inhabitants of these communes show that storms and related hazards such as flood, sea level rise and heavy rain are perceived as the most impacting climate change intensified phenomena on agriculture and aquaculture, livestock, household property and income. Opinions and measured data provided by the commune and district authorities allow estimating the total direct cost of the tropical storm at 1.56 million $US (The used conversion rate VND/$US is 21,730 when the research was conducted in 2014) during the period 2008–2013. The long-term costs of adaptation and social impact measures will be significantly higher. Details of the monetary figures allow identifying the physical and natural capital of the area as being most affected by the storm. Trend and cost analysis show that the total financial support for hazard prevention and management during 2014–2019 is estimated at 1.19 up to 1.32 million $US. Local stakeholders indicate that climate change adaptation should not be limited to technical measures such as strengthening dikes, but also should target planting protection forests and mangroves and land use planning. Financial support for the relocation policy, stakeholder involvement and integrating climate change adaptation in both the socioeconomic development master plan and local land use planning are also of importance.

Time series forecasting has an important role in many real applications in meteorology and environment to understand phenomena as climate change and to adapt monitoring strategy. This paper aims first to build a framework for forecasting meteorological univariate time series and then to carry out a performance comparison of different univariate models for forecasting task. Six algorithms are discussed: Single exponential smoothing (SES), Seasonal-naive (Snaive), Seasonal-ARIMA (SARIMA), Feed-Forward Neural Network (FFNN), Dynamic Time Warping-based Imputation (DTWBI), Bayesian Structural Time Series (BSTS). Four performance measures and various meteorological time series are used to determine a more customized method for forecasting. Through experiments results, FFNN method is well adapted to forecast meteorological univariate time series with seasonality and no trend in consideration of accuracy indices and DTWBI is more suitable as considering the shape and dynamics of forecast values.

Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.

Time series analysis and forecasting is an important tool which can be used to improve water resources management. Iraq is facing a severe water shortage problem. The use of rainwater harvesting is one of the techniques to overcome this problem. To put this into practice, it is of prime importance to forecast future rainfall events on a weekly basis. Box-Jenkins methodology has been used in this research to build Autoregressive Integrated Moving Average (ARIMA) models for weekly rainfall data from four rainfall stations in the North West of Iraq: Sinjar, Mosul, Rabeaa and Talafar for the period 1990-2011. Four ARIMA models were developed for the above stations

Time series analysis and forecasting has become a major tool in many applications in air pollution and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). In this study we used Box-Jenkins methodology to build ARIMA model for monthly ozone data taken from an Automatic Air Quality Monitoring System in Kemaman station for the period from 1996 to 2007 with a total of 144 readings. Parametric seasonally adjusted ARIMA (0,1,1) (1,1,2)12 model was successfully applied to predict the long-term trend of ozone concentration. The detection of a steady statistical significant upward trend for ozone concentration in Kemaman is quite alarming. This is likely due to sources of ozone precursors related to industrial activities from nearby areas and the increase in road traffic volume.

There  are  three  regions  of  rainfall  that  has  been  formed,  each  rainfall  regions has a variety of homogeneous and there is a correlation between rainfall stations. In  each  region  can  be determined  rainfall  prediction  model simultaneously.  The model  is  a  model  of  Vector Autoregressive  (VAR)  which  is  an extension  of  the autoregressive  model  (AR).  Based  on  this  research,  we  can  determine  the  VAR model by lag 1 or VAR (1) for each region. Region 1 (Anjatan and Sumurwatu), region  2  (Salamdarma  and  Gantar)  and  region  3  (Kedokan  Bunder  and Sudimampir), each of which has a Root Mean Square Error Prediction (RMSEP) of  3.93;  5:03;  4:48;  5.3;  2:18  and  3:53.  Correlation  value  of  observations  with predictions of rainfall respectively, 0.71; 0.62; 0:57; 0:59; 0.89, and 0.91. Keywords: AR, VAR, RMSEP, correlation

The current work presents the application of the seasonal Auto Regressive Integrated Moving Average Model (ARIMA) using the Standard Precipitation Index (SPI) as a drought indicator and then depicting the spatial distribution through geo-statistical methods. Greece is very often facing the hazardous impacts of droughts, hence presenting an almost ideal case for such an application. The applied methodology used precipitation data from 55 meteorological stations in two sets distributed throughout the country. To assess the drought, SPI was calculated for a variety of temporal steps. The statistical analyses of the examined temporal step parameters were performed and the Seasonal ARIMA model was employed. Then based on such link, forecasting attempts were derived and presented. The effort may lead to a further understanding of drought duration, magnitude and spatial extend in semi-arid areas. It is believed that such a methodology may provide some useful anticipatory information on the area’s vulnerability to drought and thus portraying the system’s susceptibility to change, damages and losses. In this context, drought contingency planning may be included in the decision making arsenal in order to also widen existing perceptions of the area’s inherent weaknesses and limited resilience to both anthropogenic and natural hazards, serving at the same time as an early warning mechanism.

Rainfall-runoff simulation and forecasting is essential for disaster management, planning, designing and operation of water resource projects. This paper reviews the till date development and current status of time series analysis-based modelling and simulation attempts of rainfall-runoff phenomenon. The discussion starts with the historical demand to forecast the chaotic natural process of rainfall-runoff. The study reveals earlier attempts for simulation of the rainfall-runoff event using regression, autoregressive, moving average, and combinations of these methods. The paper further focuses in detail about the advanced method that is Box Jenkins techniques and its derivatives (e.g. SARIMA, PARMA, TRANSFER FUNCTION etc.), for simulation of river rainfall and runoff with better accuracy. Attempt is made to include up-to-date studies for forecasting of rainfall and associated runoff using time series methods from different parts of the world and for various applications.

Problem statement: The present study proposes a new pitch detection algorithm which could potentially be used to detect pitch for disordered or pathological voices. One of the parameters required for dysphonia diagnosis is pitch and this prompted the development of a new and reliable pitch detection algorithm capable of accurately detect pitch in disordered voices. Approach: The proposed method applies a technique where the frame size of the half wave rectified autocorrelation is adjusted to a smaller frame after two potential pitch candidates are identified within the preliminary frame. Results: The method is compared to PRAAT’s standard autocorrelation and the result shows a significant improvement in detecting pitch for pathological voices. Conclusion: The proposed method is more reliable way to detect pitch, either in low or high pitched voice without adjusting the window size, fixing the pitch candidate search range and predefining threshold like most of the standard autocorrelation do.

Problem statement: Most of Seasonal Autoregressive Integrated Moving Average (SARIMA) models that used for forecasting seasonal time series are multiplicative SARIMA models. These models assume that there is a significant parameter as a result of multiplication between nonseasonal and seasonal parameters without testing by certain statistical test. Moreover, most popular statistical software such as MINITAB and SPSS only has facility to fit a multiplicative model. The aim of this research is to propose a new procedure for indentifying the most appropriate order of SARIMA model whether it involves subset, multiplicative or additive order. In particular, the study examined whether a multiplicative parameter existed in the SARIMA model. Approach: Theoretical derivation about Autocorrelation (ACF) and Partial Autocorrelation (PACF) functions from subset, multiplicative and additive SARIMA model was firstly discussed and then R program was used to create the graphics of these theoretical ACF and PACF. Then, two monthly datasets were used as case studies, i.e. the international airline passenger data and series about the number of tourist arrivals to Bali, Indonesia. The model identification step to determine the order of ARIMA model was done by using MINITAB program and the model estimation step used SAS program to test whether the model consisted of subset, multiplicative or additive order. Results: The theoretical ACF and PACF showed that subset, multiplicative and additive SARIMA models have different patterns, especially at the lag as a result of multiplication between non-seasonal and seasonal lags. Modeling of the airline data yielded a subset SARIMA model as the best model, whereas an additive SARIMA model is the best model for forecasting the number of tourist arrivals to Bali. Conclusion: Both of case studies showed that a multiplicative SARIMA model was not the best model for forecasting these data. The comparison evaluation showed that subset and additive SARIMA models gave more accurate forecasted values at out-sample datasets than multiplicative SARIMA model for airline and tourist arrivals datasets respectively. This study is valuable contribution to the Box-Jenkins procedure particularly at the model identification and estimation steps in SARIMA model. Further work involving multiple seasonal ARIMA models, such as short term load data forecasting in certain countries, may provide further insights regarding the subset, multiplicative or additive orders.

Predicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from 1961 to 2011. The model was denoted as SARIMA (1, 0, 1) (0, 1, 1)12 in this study. We first analyzed the stability and correlation of the time series. Then we predicted the monthly precipitation for the coming three yesrs. The results showed that the model fitted the data well and the stochastic seasonal fluctuation was sucessfuly modeled. Seasonal ARIMA model was a proper method for modeling and predicting the time series of monthly percipitation.

Peramalan merupakan alat bantu yang penting dalam penyusunan rencana yang efektif dan efisien. Pada beberapa literatur terdapat banyak pendekatan untuk merumuskan model peramalan. Dalam penelitian ini digunakan proses Autoregressive Integrated Moving Average (ARIMA) untuk merumuskan model peramalan curah hujan DAS Brangkal, Mojokerto, yaitu, ARR Jati Dukuh, ARR Gumeng, dan ARR Pacet Selatan. Algoritma PSO diterapkan untuk mengestimasi parameter model. Berdasarkan RMSE terkecil, model baik untuk ARR Jati Dukuh dengan RMSE 76.6505, model [ ][ ] baik untuk ARR Gumeng dengan RMSE 78.4095, sedangkan baik untuk ARR Pacet Selatan dengan RMSE 106.5537. Dengan menggunakan algoritma PSO, diperoleh model dengan RMSE 76.6505 untuk ARR Jati Dukuh, dengan RMSE 78.4095 untuk ARR Gumeng, serta dengan RMSE 106.5537 untuk ARR Pacet Selatan.

One important climate factor for tropical area is rainfall. Changes in rainfall pattern will cause numerous problems especially in agricultural activities. Rainfall pattern could also lead to either flood or drought; problems which will not only affect agricultural activities but also socio-economic situation of broad community. Therefore, study of local climate variability focusing on rainfall related to the global warming is important. Time series analysis ( correlogram and periodogram) of daily rainfall was chosen to investigate the phenomena of global warming in local scale. Data (1974-2004) was collected from Sumberjaya, Air Hitam and Fajar Bulan; three stations located inside one of the important watershed in Lampung Province. From the collelogram, in general daily rainfall in this upland and forest area shows independency up to the year of 1990. No seasonal pattern could be an indicator that rains in this area are controlled more by local topography and land cover condition then by larger scale of climate system such as monsoon. After 1990 there were some weak sign of seasonal pattern. This could be interpreted as a sign that larger climate system started influence the local rainfall and as the global warming increases, it could be predicted that local rainfall pattern will be controlled more by the larger climate system. The periodogram shows that rainfall in this area has weak annual periodic. Data from Sumberjaya on 1990-1994 and 1999-2006 showed that annual periodic were getting stronger; a sign that larger climate system started dominating the area.

In this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability of large dataset.

In this study a seasonal ARIMA model was built using Box and Jenkins method to forecast long term rainfall in Sylhet. For this purpose rainfall data from 1980 to 2010 of Sylhet station were used to build and check the model. Rainfall data from 1980 to 2006 were used to develop the model while data from 2007 to 2010 were used to verify the prediction precision. Four basic chronological steps namely: identification, estimation, diagnostic checking, and forecasting were fitted out in developing the model.  Validity of the model was tested using standard graphical explanation of residuals given by Box and Jenkins. As a second step of validation, forecasted values of monthly rainfall were checked using actual data series. After completion of necessary checking and forecast observation, the ARIMA(0, 0, 1) (1,1, 1) 12 was found to be the most effective to predict future precipitation with a 95% confidence interval. It is expected that this long term prediction will help decision makers in efficient scheduling of flood prediction, urban planning, rainwater harvesting and crop management. Keywords: Nonlinear time series analysis, ARIMA model, rainfall forecasting, Sylhet.

In this paper, an attempt to model the temporal variability of rainfall is made by performing a time series analysis on the monthly rainfall data of Jorhat from 1994 to 2013 (excluding 2003). The monthly rainfall time series showed seasonality with a prominent frequency of 0.083 cycles per year. A curve fitting technique by nonlinear regression on the original rainfall time series and on the resulting regular residuals of the subsequent fits is performed to model the seasonality of the rainfall. The selected model is capable of showing the same seasonality and frequency of rainfall variability as that of the original rainfall time series. The selected model has the potentiality to be replicated to model rainfall in places showing similar seasonality as that of the present case.

Forecasting time series has acquired immense research importance and has vast applications in the area of air pollution monitoring. This work attempts to investigate the abilities of various existing techniques when applied for short term, high granular time series forecasting of PM2.5. More specifically, a comparative study has been provided, taking into account both popularly used models and lesser-used models in this area. The study has been carried out considering ten well defined models that are ARIMA (auto-regressive integrated moving average), SARIMA (seasonal ARIMA), SES (single exponential smoothing), DES (double exponential smoothing), TES (triple exponential smoothing), ANN (artificial neural network), DT (decision tree), kNN (k-nearest neighbor), LSTM (long short-term memory) and MCFO (markov chain first order). A framework has been built that categories the models, implements them under identical execution environment and forecasts succeeding values. Implementation has been carried out over five data sets of real-world air pollution time series, that are collected from five differently located government setup monitoring stations over a period of 1 year (July 2018-June 2019). Rigorous statistical analysis has been performed that yields an insight to the nature and variability of these time series data. Forecasting has been carried out on short term basis, focusing on high granularity whereas, three different lengths of forecast horizon (1 day, 1 week, and 1 month) have been tested. Eventually, the models have been compared in terms of their associated performance measuring units namely, RMSE (root mean of squared error), MAE (mean absolute error) and MAPE (mean absolute percentage error). The comparative results verified with multiple datasets show that all the models posses less error for a shorter forecast horizon, where LSTM providing the best performance. Superiority of machine learning and deep learning models are found in case of longer length of forecast horizon with kNN achieving best accuracy whereas, significant performance degradation of ARIMA is found for longer forecast horizon. Moreover, TES, DT, kNN, LSTM, MCFO are found to be well adopted in relation with shape and variability of the data. Note that the performance on various length of high granular forecast horizon have been studied over multiple datasets that give an added value to this work.

Collaborative information foraging refers to the collective activities of seeking and handling information in order to meet information needs. This paper delineates requirements for modeling salient factors that shape collaborative information foraging behavior of groups. Existing modeling approaches are assessed based on their adequacy for measuring identified salient factors that shape collaborative information foraging behavior. A view of information foraging behavior as a dynamic process is presented. Consequently, this paper purports that modeling methods employed to aid understanding of foraging behavior must allow for plausible explanation of the inherent dynamism in foraging activities. This work therefore provides an initial roadmap to defining salient factors that need to be addressed in order to adequately model collaborative information foraging behavior within teams that operate in extreme environments. Implications of this work in practice and research are discussed.

Accurate prediction of precipitation is a challenging task because of the non-linearity and randomness involved in the processes. Most of the conventional models fail to capture this non-linearity and randomness. In the present study, data driven techniques like artificial neural network (ANN) and model tree (MT) are used for predicting the short-term precipitation. Several models are developed using these techniques by changing the parameters and the results are compared with conventional techniques such as multi-linear regression (MLR) and auto regressive integrated moving average (ARIMA) models. The effect of smoothing and pruning is also studied in MT models. The study shows that MT performed better than other techniques with a correlation of 0.70. The advantage of MT over ANN is that, the MT gives several linear equations, which can be easily integrated into other models. Thus, it may be concluded that the highly erratic daily rainfall could be mapped and predicted better using data driven techniques.

Abstract. The GSMaP_MVK+ (Global Satellite Mapping of Precipitation) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment in Northeast Jordan for the period of January 2003 to March 2008. The scarcity of the ground rain gauge network alone did not adequately show the detailed structure of the rainfall distribution, independent form interpolation techniques used. This study combines GSMaP_MVK+ and ground rain gauges to produce accurate, high-resolution datasets. Three meteorological stations and six rain gauges were used to adjust and compare GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauges records showed distinct regions where they correlate, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The "best" fit of MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reasonable than estimates obtained using daily data. The adjusted GSMaP_MVK+ performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales while daily estimation showed some weakness in light and moderate storms.