Bengkel netwon usaha tambah baik kualiti hidup Orang Asli belum

GRIK (BELUM) – Kumpulan Kejuruteraan Air, Jabatan Kejuruteraan Awam, Fakulti Kejuruteraan, Universiti Putra Malaysia (UPM) menganjurkan bengkel mengenalpasti masalah Orang Asli di Kampung Sungai Kejar dan Kampung Sungai Tiang, Hutan Belum, Perak

The hydrology of the Peruvian Amazon river and its sensitivity to climate change

This PhD thesis explores the utility of a land surface model (Joint UK Land-Environment Simulator, JULES) for large-scale hydrological modelling of the Peruvian Amazon - a humid tropical mountain basin where process understanding is poor and data are scarce. A sparse rain gauge network necessitates the use of large-scale data from satellite and global climate model reanalysis to complement ground observations, commanding a closer look at (1) the uncertainties (2) merging techniques to utilise multiple observations in the model forcing. A main outcome of the research is establishing the model’s sensitivity to precipitation error, and at the same time, demonstrating an increasing reliability of global remote sensing products as model forcing, specifically, with data from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis version 7 algorithm. Furthermore, satellite-rain gauge data assimilation techniques such as mean-bias correction, double smoothing residual blending, and Bayesian combination, are shown to reduce the mean errors in the satellite-based product. Secondly, with regional calibration and an offline runoff routing scheme, JULES is shown to be reasonably skillful at reproducing the observed streamflow dynamic and extremes. Representing the subgrid heterogeneity of soil moisture using the probability distributed model (PDM) was key to improving surface runoff generation. However, evapotranspirative fluxes in the lower basin remain poorly reproduced without an adequate floodplain system representation. Finally, under the Intergovernmental Panel for Climate Change’s RCP4.5 future climate scenario, which projects a warming and wetting up to the year 2035, the Peruvian Amazon basin is shown to respond nonlinearly to the increase in wet season precipitation with more than 40% increase in the peak flows compared to the baseline scenario. There is limited confidence in the projections due to climate projections uncertainty and the assumptions of model stationarity.Open Acces

Early prediction system using neural network in Kelantan River, Malaysia

Flood is a major disaster that happens around the world. It has caused the loss of many precious lives and destruction of properties. The possibility of flood can be determined by many factors that consist of rainfall, water flow rate and water level. This project aims to design a water level prediction system which is used to analyse the Kelantan River water level based on Sokor River, Galas River and Lebir River Flow rate and rainfall of at Ladang Kuala Nal and Ladang Kenneth. The system utilizes neural networks in predicting the water level for 5 hours ahead. This system has 5 inputs and 1 output prediction. This prediction system focuses on comparing the conventional method and the NNARX system in the determining the possibility of flood. The result shows that the NNARX have higher performance in predict the water level of Kelantan River in comparing to the conventional method. The performance of the system is based on the value of the means square error (MSE). The MSE of the conventional method is 0.2250 meanwhile for NNARX is 1.342 x 10-4. In ensuring the NNARX model capability and flexibility, another case study was tested with same of input and output but with different period. The performance for the model is 3.917 x 10-4 and is proven it can be used to different set of data

A Promising Wavelet Decomposition –NNARX Model to Predict Flood: Application to Kelantan River Flood

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2

Impact assessment of future climate change on streamflows upstream of Khanpur Dam, Pakistan using soil and water assessment tool

The study aims to evaluate the long-term changes in meteorological parameters and to quantify their impacts on water resources of the Haro River watershed located on the upstream side of Khanpur Dam in Pakistan. The climate data was obtained from the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) for MIROC-ESM model under two Representative Concentration Pathway (RCP) scenarios. The model data was bias corrected and the performance of the bias correction was assessed statistically. Soil and Water Assessment Tool was used for the hydrological simulation of watershed followed by model calibration using Sequential Uncertainty Fitting version-2. The study is useful for devising strategies for future management of Khanpur Dam. The study indicated that in the future, at Murree station (P-1), the maximum temperature, minimum temperature and precipitation were anticipated to increase from 3.1 °C (RCP 4.5) to 4.0 °C (RCP 8.5), 3.2 °C (RCP 4.5) to 4.3 °C (RCP 8.5) and 8.6% to 13.5% respectively, in comparison to the baseline period. Similarly, at Islamabad station (P-2), the maximum temperature, minimum temperature and precipitation were projected to increase from 3.3 °C (RCP 4.5) to 4.1 °C (RCP 8.5), 3.3 °C (RCP 4.5) to 4.2 °C (RCP 8.5) and 14.0% to 21.2% respectively compared to baseline period. The streamflows at Haro River basin were expected to rise from 8.7 m3/s to 9.3 m3/s

Assessment of streamflow simulation for a tropical forested catchment using dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales

Remotely sensed relative humidity for predicting Metisa plana's population oil palm plantations

Metisa plana (Walker) is leaves defoliating insect that is able to cause a staggering loss of USD 2.32 billion within two years to Malaysian oil palm industry. Therefore, an early warning system to predict the outbreak of Metisa plana that is cost, time, and energy effective is crucial. In order to do this, the role of environmental factors such as relative humidity (RH) on the pests’ population’s fluctuations should be well understood. Hence, this study utilized the geospatial technologies to i) to construct the relationship between the geospatially derived relative humidity and Metisa plana outbreak, and ii) to predict the outbreak of Metisa plana in oil palm plantation. Metisa plana census data of larvae instar 1, 2, 3, and 4 were collected approximately biweekly over the period of 2014 and 2015. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images providing values of RH were extracted and apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) prior to census date. Pearson’s correlation, multiple linear regression (MLR) and multiple polynomial regression analysis (MPR) were carried out to analyse the linear relationship between Metisa plana and RH. Artificial neural network (ANN) was then used to develop the best prediction model of Metisa plana’s outbreak. Results show that there are correlations between the presence of Metisa plana with RH, however, the time lag effect was not prominent. MPR was able to produce model with higher R2 in comparison to MLR with the highest R2 for both analysis were 0.48 and 0.15 respectively at T4 to T6. Model with the highest accuracy was achieved by ANN that utilized the RH at T1 to T3 at 95.29%. Based on the result of this study, the prediction of Metisa plana’s landscape ecology was possible with the utilization of geospatial technology and RH as the predictor parameter

Contrasting influences of seasonal and intra-seasonal hydroclimatic variabilities on the irrigated rice paddies of Northern Peninsular Malaysia for weather index insurance design

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia